Data Engineer/Consultant (Senior/Staff)

🏢 Summary: Design and build a modern data platform from scratch for an insurance client, covering architecture, data ingestion, modelling, and production operations. The role focuses on establishing a governed, scalable Snowflake-based environment to enable reliable reporting and AI capabilities. You will take ownership across the full data lifecycle, from requirements definition to deployment and maintenance. 🗂️ Requirements: Hands-on experience with Python, Proven experience with data warehouse solutions (BigQuery, Redshift or Snowflake), Experience with Databricks or data lakehouse platforms, Strong expertise in data modelling and ETL/pipeline design and maintenance, Experience with AWS, GCP or Azure cloud services, Ability to design and build data ingestion and processing pipelines, Experience working in Agile environment, Understanding of data governance and data quality concepts 📃 Skills: Python, SQL, Snowflake, BigQuery, Redshift, Databricks, Azure, AWS, GCP, Terraform, dbt, PowerBI, Spark, ETL, CI/CD 🏢 Description: We are #VLteam – tech enthusiasts constantly striving for growth. The team is our foundation, that’s why we care the most about the friendly atmosphere, a lot of self-development opportunities and good working conditions. Trust and autonomy are two essential qualities that drive our performance. We simply believe in the idea of ​​“measuring outcomes, not hours”. Join us & see for yourself! About the role The majority of these roles will be at the forefront of client collaboration and building VL positions in the industry (spearheading projects). You will work closely and directly with a different specialist from the client side. Collaborate with stakeholders to define requirements, develop data pipelines and data quality metrics. You will participate in defining the requirements and architecture for the new platform, implement the solution, and remain involved in its operations and maintenance post-launch Your work will also introduce data governance and management, laying the foundation for accurate and comprehensive reporting that was previously impossible. Build data ingestion & processing pipelines. All of the above with a strong focus on the customer’s needs. Flexibility in action and the ability to overcome obstacles are highly valued in this role. View available projects: Project Data Foundation & AI Enablement Project Scope We are architecting a modern Data Platform for a fast-scaling client in the Insurance sector. Our work consolidates fragmented legacy systems, organises data from a vast number of sources, and establishes a standardised, governed, and future-proof data foundation. We aim to unlock the full value of the company’s data, enabling faster, informed decision-making and providing the backbone for business growth and AI readiness. Tech stack SQL, Python, Snowflake, dbt, Data modelling, Data quality, Power BI, Azure, Terraform Challenges The primary objective is to deliver a robust data foundation and enable AI capabilities for a client that has grown organically. The work focuses on several key areas: Establishing a production-ready, fully operational Snowflake environment and driving operational excellence. Translating complex business logic into accurate data models to ensure the platform truly reflects business reality. Integrating diverse data sources to build reliable data products and comprehensive data dictionaries. Managing the full Data Engineering and Data Science lifecycle to support production ML and AI experimentation. Taking ownership from concept to deployment. Cultivating an engineering mindset by promoting automation, CI/CD, and rigorous standards. Team We are building a small (4-6 people), agile, cross-functional team capable of delivering the complete data platform, from initial architecture to production operations. Roles involved: DevOps, Data Engineer, Snowflake Specialist, MLOps/AI Engineer, Business Analyst (BA). The team will collaborate closely with business stakeholders to ensure effective knowledge transfer and strict alignment with strategic goals. Team The team is small but highly motivated, taking on a broad scope of responsibilities as the platform is built and expanded. What we expect in general A proactive approach and flexibility in action were a must Very good command of English (written and spoken) Hands-on experience with Python Proven experience with data warehouse solutions (e.g., BigQuery, Redshift, Snowflake) Experience with Databricks or data lakehouse platforms Strong background in data modelling, data catalogue concepts, data formats, and data pipelines/ETL design, implementation and maintenance Ability to thrive in an Agile environment, collaborating with team members to solve complex problems with transparency Experience with AWS/GCP/Azure cloud services, including: GCS/S3/ABS, EMR/Dataproc, MWAA/Composer or Microsoft Fabric, ADF/AWS Glue Experience in ecosystems requiring improvements and the drive to implement best practices as a long-term process Experience with Infrastructure as Code practices, particularly Terraform, is an advantage Proactive approach Don’t worry if you don’t meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more! Apply now

🏢 Summary: Data Engineer role focused on designing and scaling data infrastructure, ETL pipelines, and Lakehouse architecture for a manufacturing environment supporting analytics and AI workloads. The position involves building operational data systems, ensuring data quality, and integrating industrial and manufacturing data sources. Candidates will collaborate cross-functionally and help establish scalable data architecture standards for future facility growth. 🗂️ Requirements: 8+ years of data engineering or data infrastructure experience, Experience designing data lakes or Lakehouse platforms, Experience building ETL/ELT pipelines, Strong data modeling expertise, Experience with relational databases, Strong SQL skills, Ability to document architecture and technical decisions, Experience collaborating with technical and non-technical stakeholders, U.S. Person status for export-controlled access 📃 Skills: SQL, PostgreSQL, SQLServer, ETL, ELT, Lakehouse, Python, Airflow, Prefect, dbt, InfluxDB, TimescaleDB, MQTT, DeltaLake, ApacheIceberg, AWS, Azure, GCP 🏢 Description: Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, we serve critical industries such as defense, aerospace, and automotive, powering a high-technology future. Vulcan Elements is building a team of ambitious professionals committed to Mission Focus, Technical Excellence, and Transparency. As the Data Engineer, you will design and build the data infrastructure that makes Vulcan's operational and business data useful — first at pilot scale, and then as the foundation for a 10,000 ton/year facility. You will work from architecture to implementation: evaluating and selecting platforms, designing data models and pipelines, and building the systems that collect, contextualize, and deliver data to the teams and tools that depend on it. You will collaborate closely with cross-functional stakeholders to translate operational requirements into a durable, scalable data architecture. As Vulcan grows, this role has the opportunity to expand into a team leadership position. Responsibilities Architecture & Platform Design - Design and own Vulcan's data architecture from operational data stores through ETL pipelines to the analytics and AI layer - Evaluate and select platforms for the data Lakehouse, ETL tooling, and operational databases, weighing scalability, compliance requirements, operational burden, and cost - Review, refine, and implement data architecture design documents, ensuring designs are technically sound and account for CUI and ITAR data handling requirements - Make and document key platform and design decisions with enough clarity that future team members can understand the reasoning and build on it - Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign - Apply sound engineering practices to everything you build: version control, testing, observability, and documentation, and hold those standards as the data team grows Data Pipeline & Integration - Design and build ETL pipelines that move data from operational data stores into the data Lakehouse with full contextual enrichment, making it ready for analytics and AI workloads - Build reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems - Collaborate across engineering, operations, and IT to understand data flows, dependencies, and integration requirements, and translate them into pipeline and architecture decisions - Identify and eliminate manual data workflows, replacing them with monitored, reliable pipelines - Diagnose and resolve data quality issues across the stack, and build monitoring into pipelines so problems surface early Data Modeling & Quality - Define data models that support operational queries, analytical workloads, and future AI and ML applications - Own data contextualization standards ensuring every data point carries the metadata needed to make it meaningful - Contribute to schema design and payload definitions for operational data stores, working toward consistency and legibility across the organization - Support the development of reporting and visibility tools that give operations and leadership clear insight into process and quality data - Write clear technical documentation for architecture decisions, data models, pipeline designs, and operational runbooks Responsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business. Qualifications - 8+ years of experience in data engineering, data infrastructure, or a closely related technical role with a track record of owning and delivering production systems - Demonstrated experience designing and building data lakes, Lakehouses, or analytical data stores; understands the tradeoffs between platforms and can make and defend platform selection decisions - Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data - Deep fluency with data modeling for both operational and analytical workloads; can design schemas that serve present needs without foreclosing future ones - Experience with relational databases (PostgreSQL, SQL Server, or similar); writes and debugs SQL confidently - Comfortable working in a fast-moving environment with a small team, making decisions with incomplete information and documenting them clearly for future colleagues - Strong communicator who can work across technical and non-technical stakeholders and translate between operational requirements and data architecture decisions - Must be a U.S. Person due to required access to U.S. export-controlled information or facilities Desired Skills - Experience with time-series databases (InfluxDB, TimescaleDB, or similar) common in industrial and IoT environments - Familiarity with industrial data concepts — historian data, process tags, OT/IT integration — and the data challenges specific to manufacturing environments - Experience working on or alongside a Unified Namespace or MQTT-based data architecture; understands how industrial messaging infrastructure relates to the data layer - Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar) - Experience with ETL orchestration tooling (Airflow, Prefect, dbt, or similar) - Comfort with scripting and lightweight development (Python, SQL, or similar) for pipeline development and data quality tooling - Familiarity with cloud platforms (AWS, Azure, or GCP) and experience evaluating on-premises vs. cloud tradeoffs for data infrastructure - Experience working in a controlled information environment; familiarity with the handling requirements for Controlled Unclassified Information (CUI) or export-controlled technical data under ITAR or EAR - Experience in a manufacturing, industrial, or operations-heavy environment

🏢 Summary: Data Engineer role focused on designing and scaling data infrastructure, ETL pipelines, and Lakehouse architecture for manufacturing operations supporting analytics and AI workloads. The position involves building reliable industrial data systems, defining data models, and collaborating across engineering and operations teams in a secure, compliance-driven environment. There is potential for future leadership responsibilities as the organization grows. 🗂️ Requirements: 8+ years of experience in data engineering or data infrastructure, Experience designing and building data lakes or Lakehouse platforms, Experience building ETL/ELT pipelines, Strong data modeling experience for operational and analytical workloads, Experience with relational databases, Strong SQL skills, Ability to work in fast-moving environments with small teams, Ability to communicate across technical and non-technical stakeholders, U.S. Person status for access to export-controlled information 📃 Skills: PostgreSQL, SQLServer, SQL, ETL, ELT, Lakehouse, Python, InfluxDB, TimescaleDB, MQTT, DeltaLake, Iceberg, Airflow, Prefect, dbt, AWS, Azure, GCP 🏢 Description: Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, the company serves critical industries such as defense, aerospace, and automotive. As the Data Engineer, you will design and build the data infrastructure that makes operational and business data useful — first at pilot scale, and then as the foundation for a 10,000 ton/year facility. You will work from architecture to implementation: evaluating and selecting platforms, designing data models and pipelines, and building the systems that collect, contextualize, and deliver data to the teams and tools that depend on it. You will collaborate closely with cross-functional stakeholders to translate operational requirements into a durable, scalable data architecture. As the organization grows, this role has the opportunity to expand into a team leadership position. Responsibilities Architecture & Platform Design - Design and own data architecture from operational data stores through ETL pipelines to the analytics and AI layer - Evaluate and select platforms for the data Lakehouse, ETL tooling, and operational databases, weighing scalability, compliance requirements, operational burden, and cost - Review, refine, and implement data architecture design documents, ensuring designs are technically sound and account for CUI and ITAR data handling requirements - Make and document key platform and design decisions with enough clarity that future team members can understand the reasoning and build on it - Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign - Apply sound engineering practices to everything you build: version control, testing, observability, and documentation, and hold those standards as the data team grows Data Pipeline & Integration - Design and build ETL pipelines that move data from operational data stores into the data Lakehouse with full contextual enrichment, making it ready for analytics and AI workloads - Build reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems - Collaborate across engineering, operations, and IT to understand data flows, dependencies, and integration requirements, and translate them into pipeline and architecture decisions - Identify and eliminate manual data workflows, replacing them with monitored, reliable pipelines - Diagnose and resolve data quality issues across the stack, and build monitoring into pipelines so problems surface early Data Modeling & Quality - Define data models that support operational queries, analytical workloads, and future AI and ML applications - Own data contextualization standards ensuring every data point carries the metadata needed to make it meaningful - Contribute to schema design and payload definitions for operational data stores, working toward consistency and legibility across the organization - Support the development of reporting and visibility tools that give operations and leadership clear insight into process and quality data - Write clear technical documentation for architecture decisions, data models, pipeline designs, and operational runbooks Responsibilities and tasks outlined are not exhaustive and may change as determined by business needs. Qualifications - 8+ years of experience in data engineering, data infrastructure, or a closely related technical role with a track record of owning and delivering production systems - Demonstrated experience designing and building data lakes, Lakehouses, or analytical data stores; understands the tradeoffs between platforms and can make and defend platform selection decisions - Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data - Deep fluency with data modeling for both operational and analytical workloads - Experience with relational databases (PostgreSQL, SQL Server, or similar) - Writes and debugs SQL confidently - Comfortable working in a fast-moving environment with a small team - Strong communicator able to work across technical and non-technical stakeholders - Must be a U.S. Person due to required access to U.S. export-controlled information or facilities Desired Skills - Experience with time-series databases (InfluxDB, TimescaleDB, or similar) - Familiarity with industrial data concepts including historian data, process tags, and OT/IT integration - Experience with Unified Namespace or MQTT-based data architecture - Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar) - Experience with ETL orchestration tooling (Airflow, Prefect, dbt, or similar) - Comfort with scripting and lightweight development (Python, SQL, or similar) - Familiarity with cloud platforms (AWS, Azure, or GCP) - Experience working in controlled information environments with CUI, ITAR, or EAR requirements - Experience in manufacturing, industrial, or operations-heavy environments

🏢 Summary: Senior Infrastructure Engineer role focused on maintaining, scaling, and optimizing large-scale on-premise developer infrastructure for a major investment bank, supporting over 10,000 users. The position involves ownership of core developer tools, automation, performance troubleshooting, and integration of LLM-based tooling in a secure enterprise environment. 🗂️ Requirements: 5+ years commercial experience in system administration or infrastructure engineering, Strong Linux administration expertise, Deep understanding of networking concepts, Hands-on experience with Jenkins, Bitbucket, and BuildBarn, Proficiency in Bash and Python scripting, Ability to troubleshoot and resolve large-scale performance issues, Experience supporting on-premise infrastructure, Ability to work across Linux, Windows, and macOS environments, Experience in large-scale Enterprise or financial environments, English proficiency at B2/C1 level 📃 Skills: Linux, Windows, macOS, Networking, Jenkins, Bitbucket, BuildBarn, Bash, Python, PowerShell, Git, AWS, Azure, JVM, C++, Bazel, MCP, AI-agents, Claude, Copilot, Citrix, JIRA 🏢 Description: We are #VLteam – tech enthusiasts constantly striving for growth. The team is our foundation, that’s why we care the most about the friendly atmosphere, a lot of self-development opportunities and good working conditions. Trust and autonomy are two essential qualities that drive our performance. We simply believe in the idea of ​​“measuring outcomes, not hours”. Join us & see for yourself! About the role You will join our team to maintain, scale, and optimise our core on-premise infrastructure for a major investment bank. Your main goal will be to take ownership of essential developer tools and services including Jenkins, Bitbucket, and BuildBarn ensuring their highest reliability for on-prem installations for a user base of over 10,000 developers. We rely on strong system administration skills, deep networking knowledge, and automation to keep our environments running smoothly. While 99% of our infrastructure is Linux-based, our team supports systems across Linux, Windows, and macOS ecosystems. If you join us as a Senior Engineer, you will also lead the diagnosis and resolution of complex performance bottlenecks across this large-scale infrastructure. Project scope We are working with the core developer tooling team responsible for 30k+ developers in one for the biggest investment bank. Our team is especially focused on LLM-based tools for developers – evaluation, trials, onboarding and customisation. After the phase of initial tests, the team now faces a need to onboard 1000s of new developers each week to use AI tooling. The team works closely with vendors and in-house teams to ensure that tools like Claude Code or Copilot CLI bring as much value as possible to the client developers. The team will work on automations, setup, MCPs and more.The team supports users – mainly via running trials and checks and helping with escalations from existing, hands-on support teams Tech stack Python, MCP, AI-agents, scripting, git, AWS, Azure, JVM, C++, Bazel Tools and Workflow Claude Code, Amp, Intellij, git, Kanban, Windows via Citrix, JIRA, BitBucket Challenges Customising LLM tools to fit the client’s environment and flows. Working on integration of existing and new context sources including various MCP. Tracking user’s needs and building a generic mechanism that fits multiple teams. The team will need to balance security requirements with pragmatism and users’ experience Team 4 people in the VL team working with bigger teams on the client’s side – distributed across Americas, Europe and Asia What we expect in general Solid background in Linux administration and a deep understanding of networking concepts. Hands-on experience in managing and optimising on-premise developer tools (such as Jenkins, Bitbucket, and BuildBarn). Proficiency in scripting languages (Bash and Python; basic knowledge of PowerShell is an asset) to automate administrative tasks. Strong analytical skills to diagnose, troubleshoot, and solve system performance problems at scale. Readiness to support a multi-platform environment (primarily Linux, with secondary support for Windows and macOS). Commercial experience working within large-scale Enterprise or financial environments is highly preferred. At least 5 years of commercial experience in system administration or infrastructure engineering. English skills at a [B2/C1] level, allowing for seamless communication. A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more!

🏢 Summary: Software engineering role in the Data Platform team focused on building and maintaining data-driven applications, including AI-powered data access tools. The position involves developing scalable data and metadata solutions and contributing to high-impact projects across the organization. You will work across legacy and greenfield systems while leveraging modern AI-assisted development practices. 🗂️ Requirements: 3+ years of software engineering experience, Proficiency in Python, Proficiency in SQL 📃 Skills: Python, SQL, Kubernetes, Kafka, Trino, Grafana, Prometheus, CloudWatch, AWS, Copilot, Claude, Codex, AI, Cloud, Data, Metadata 🏢 Description: Join the Data Platform team to build and maintain data-driven applications, including AI-powered data access tools. Work on a mix of legacy, improved, and greenfield projects with firm-wide impact. What you’ll do: Develop and enhance solutions for data and metadata access Build reliable, scalable applications and operational processes Experiment with AI-assisted development tools and share best practices Collaborate in a distributed team, taking ownership of impactful projects What we’re looking for: 3+ years in software engineering, Python and SQL experience Experience with data systems, cloud technologies, or AI-assisted tools is a plus Product-oriented mindset, comfortable navigating ambiguity, and impact-focused Nice to have: Experience with Kubernetes, Kafka, or federated query engines (e.g., Trino) Familiarity with monitoring/observability tools like Grafana, Prometheus, CloudWatch Experience with AWS or other cloud platforms Hands-on experience with AI coding assistants (Copilot, Claude Code, Codex, etc.) Why join: High-visibility, high-impact role across the firm Shape AI-driven development and data access practices Work on cutting-edge tech in institutional finance Collaborative culture with competitive compensation

🏢 Summary: Lead Data Engineer role focused on driving architecture and leading a team to build scalable, secure ETL/ELT pipelines and analytics-ready data models on modern cloud platforms. The position combines hands-on engineering with technical leadership, ensuring high standards in governance, observability, and performance optimisation. You will shape data infrastructure that supports large-scale analytics across the organisation. 🗂️ Requirements: Proven experience leading data engineering or analytics engineering teams, Strong programming skills in SQL, Strong programming skills in Python, Hands-on experience with Airflow or Prefect in production, Deep practical experience with dbt, Experience with Snowflake or Databricks at scale, Strong knowledge of dimensional modelling and SCD strategies, Experience implementing data quality and governance frameworks, Experience with CI/CD and automated testing in data systems 📃 Skills: SQL, Python, Airflow, Prefect, dbt, Snowflake, Databricks, ETL, ELT, CI/CD, SCD, Dimensional, Git, IaC 🏢 Description: ABOUT THE COMPANY Our client is a global legal technology company that has been building software for the legal industry for over two decades. Our AI-powered cloud platform is used by leading law firms, Fortune 500 corporations, and government agencies worldwide to organise complex data, surface critical insights, and act on them — across litigation, investigations, regulatory inquiries, and data breach response. We're valued at $3.6 billion and invest over $170 million annually in R&D. We're making substantial investments in data lake technology and distributed systems to support future growth and advanced analytics. Our scale means the data problems here are genuinely hard — and the infrastructure you lead will have real consequence across the organisation. ABOUT THE ROLE We're looking for a Lead Data Engineer to combine deep technical expertise with hands-on team leadership, guiding a team of data engineers building and maintaining ETL/ELT pipelines, data models, and governance frameworks that power analytics and reporting across the organisation. This is a technical leadership role — you'll drive architectural decisions, mentor engineers, and ensure delivery of secure, reliable, and scalable data solutions. You'll collaborate closely with stakeholders to align technical work with business objectives, champion governance and observability standards, and foster a culture of continuous improvement. The expectation is that you're equally effective in an architecture review as you are pairing with an engineer on a tricky pipeline problem. WHAT YOU'LL WORK ON Team leadership and mentorship Lead and mentor a team of data engineers, promoting collaboration, knowledge sharing, and professional growth. Set the standard for engineering quality and hold the bar consistently. Architecture and pipeline design Drive architectural decisions for ETL/ELT pipelines, orchestration frameworks (Airflow/Prefect), and transformation layers (dbt). Facilitate architecture reviews and contribute to design decisions for scalable, fault-tolerant systems. Analytics data modelling Oversee design and implementation of analytics-ready data models — dimensional schemas, SCD strategies, and semantic layers — that internal teams can build on reliably. Engineering best practices Ensure adherence to clean code, modular design, CI/CD, automated testing, and code review standards across all data engineering work. Platform optimisation Manage performance tuning and cost optimisation for Snowflake, Databricks, and related cloud data platforms at scale. Governance and observability Champion governance, observability, and compliance frameworks across all data workflows — including data quality, lineage tracking, and multi-tenant environment controls. Stakeholder communication Communicate effectively with leadership and cross-functional teams to provide updates, resolve blockers, and ensure timely delivery aligned with business objectives. WHAT WE LOOK FOR Proven technical team leadership Demonstrated experience leading data engineering or analytics-focused development teams — mentoring engineers, driving architectural decisions, and owning delivery outcomes. SQL and Python Strong programming skills in both SQL and Python, applied to production data systems at scale. ETL/ELT orchestration Hands-on experience with orchestration tools — Airflow and/or Prefect — in production pipeline environments. dbt expertise Deep practical experience with dbt for transformation workflows and analytics modelling, including testing, documentation, and modular project design. Snowflake and Databricks Familiarity with Snowflake and/or Databricks for large-scale data processing, including performance tuning and cost management. Data modelling principles Solid understanding of data modelling principles, incremental strategies, and schema design for analytics — dimensional modelling, SCDs, and semantic layer design. Governance and data quality Knowledge of data quality frameworks, lineage tracking, and governance in multi-tenant environments. Software engineering practices Familiarity with CI/CD, automated testing, and infrastructure-as-code practices applied to data systems. Communication and stakeholder management Strong communication skills with the ability to operate confidently across technical teams and business stakeholders. THE TEAM You'll join a global engineering organisation working on a platform used by some of the world's largest legal teams. The culture is diverse, inclusive, and driven by high standards. Engineers here work on genuinely complex technical problems at scale — and are supported with the coaching, development, and tooling to keep growing. COMPENSATION & BENEFITS Salary 270,000 – 406,000 PLN per year, plus an annual performance bonus and long-term incentives. Health coverage Comprehensive health, dental, and vision plans. Parental leave Parental leave available for both primary and secondary caregivers. Flexible working Flexible work arrangements, hybrid model. Company breaks Two week-long company-wide breaks per year, plus additional time off. Training investment Dedicated training investment programme to support ongoing professional development.

🏢 Summary: Fullstack Data & AI Engineer role focused on building and maintaining end-to-end applications using .NET/C# and Angular, while designing data pipelines and integrating AI/LLM features. The position combines full-stack development with cloud data warehouse work and ETL processes to deliver scalable, AI-powered solutions. Responsibilities include performance optimization, Azure-based deployments, and technical support. 🗂️ Requirements: Bachelor’s degree in Computer Science, Information Systems, or related field, Minimum 6 years of .NET/C# and T-SQL full-stack development experience, Strong experience with ASP.NET, Angular, REST, Entity Framework, Hands-on experience with SQL Server and T-SQL, Experience with cloud data warehouses (Redshift, BigQuery, Snowflake, or Azure Synapse), Experience designing and maintaining ETL/ELT data pipelines, Practical experience integrating LLMs and AI tools, Minimum 2 years of experience with Azure-hosted applications, Understanding of secure coding practices, Experience with Azure DevOps 📃 Skills: C#, .NET, ASP.NET, Angular, T-SQL, SQL, SQLServer, EntityFramework, REST, JavaScript, Azure, Redshift, BigQuery, Snowflake, AzureSynapse, ETL, ELT, LLM, AI, AzureDevOps, MessageQueues 🏢 Description: Vecten is an AI-native data and technology partner for private equity, venture capital, and healthcare. We build proprietary data infrastructure, deploy AI solutions, and own the outcomes — not just the code. Our clients manage a cumulative $210B+ in assets and stay with us for an average of five years. We've reorganized our own company around AI, and we do the same for the firms that trust us with their most critical decisions. The project: The primary role of the Fullstack Data & AI Engineer is to build and maintain software applications using .NET/C# and Angular, while also designing and supporting data pipelines and AI-powered features. The person will create applications from scratch as well as modify existing ones, including configuration, testing, and deployment. Beyond application development, you will work with cloud data warehouses, ETL processes, and LLM integrations to deliver end-to-end solutions. You will also provide application support and mentor junior team members. On a daily basis, you will write and debug code, work with large datasets, and collaborate with software engineers, QA analysts, product managers, business analysts, and production support engineers to build software solutions that meet client business needs. The role: Write clean, scalable, and secure code using T-SQL and .NET/C# for applications hosted on-prem and in Azure Develop front-end and back-end components using Angular, .NET Core, C#, T-SQL, JavaScript, Message Queues, and related technologies Design, implement, and maintain data pipelines and ETL/ELT processes, including ingestion, transformation, and loading into cloud data warehouses such as Redshift or BigQuery Build and maintain efficient database schemas, stored procedures, and queries using SQL Server; optimize queries and application code for performance and stability Integrate LLM-based features and AI tools into applications and development workflows Revise, update, refactor, and debug code to troubleshoot errors and improve quality Follow Agile methodology and participate in team ceremonies (Standup, Retro, Planning, Refinement, etc.) Participate in requirements analysis and refinement, updating requirements as needed Produce and execute unit tests Participate in code reviews and both provide and incorporate feedback Collaborate with internal teams to produce and document scalable software design and architecture Contribute to improving development processes, engineering tools, code quality, automation, and the SDLC Serve as a technical expert on applications and provide Level 2/3 support via Helpdesk tickets as needed What's important for us? Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience At least 6 years of .NET/C# and T-SQL full-stack development experience, including C#, ASP.NET , SQL Server (T-SQL), REST, Angular, and Entity Framework Hands-on experience with cloud data warehouses such as Redshift, BigQuery, Snowflake, or Azure Synapse, including designing schemas, writing complex queries, and managing large datasets Practical experience building and maintaining data pipelines and ETL/ELT processes AI-forward mindset with practical experience using LLMs and AI tools to improve development workflows and application capabilities 2+ years of experience developing and supporting applications hosted in Azure Good understanding of secure coding guidelines and the ability to apply them in practice Experience working with Healthcare EDI transactions is a plus; 1-2 years of Healthcare or Insurance industry experience is a plus Strong analytical, critical thinking, and problem-solving skills, combined with solid interpersonal and communication skills High attention to detail; able to organize and prioritize work while maintaining accuracy Ability to work independently and follow through on assignments with minimal direction Working knowledge of Azure DevOps or ability to learn it quickly What do we offer? Culture of teamwork, professional development, and knowledge sharing ( https://www.youtube.com/user/sunscraperscom ) Flexible working hours and remote work possibility Multisport card Private medical care Culture of good feedback: code reviews, evaluation meetings, mentoring. Comfortable office in central Warsaw equipped with all the necessary tools for comfortable work (Macbook Pro, external screen, ergonomic chairs) - if working on-site

🏢 Summary: Remote Data Engineer role focused on building and evolving an internal data platform that enables secure, governed, and scalable access to data for software development and analytics teams. The position involves designing, implementing, and maintaining AWS-based data infrastructure, data pipelines, and platform services while ensuring compliance, resiliency, and automation. The role also includes architectural improvements, mentorship, and driving best practices in modern data architectures. 🗂️ Requirements: B.S. in Computer Science or equivalent experience, 4+ years of SQL experience, 4+ years of relational database design and programming (PostgreSQL, MySQL), Experience managing AWS data infrastructure (S3, RDS, Kafka, Glue, Athena, etc.), Experience in software development or systems engineering (Java, JavaScript, Python, PHP), Experience with modern API platform design and security practices, Knowledge of event-driven architecture and change data capture, Legal authorization to work in the United States without visa sponsorship 📃 Skills: SQL, PostgreSQL, MySQL, PL/pgSQL, AWS, S3, RDS, Kafka, Glue, Athena, Quicksight, DataZone, Java, JavaScript, Python, PHP, API, CI/CD, Docker, Kubernetes, Terraform, NetSuite, Salesforce, Workday, Pendo, Grafana, Kibana, Elasticsearch, OpenTelemetry, ETL, Boomi, HIPAA, PCI, SEC, FINRA, Agile, Kanban 🏢 Description: About SRS Acquiom SRS Acquiom delivers the smartest way to run a deal™ through a platform and services designed to help deal parties manage complex M&A and loan agency transactions more efficiently. Based in Denver, Colorado, with offices across the United States and in London and Amsterdam, SRS Acquiom supports transactions across North America and Europe. Our M&A services include professional shareholder representation, paying and escrow agent services, and online document solicitation and reporting. For loan and credit transactions, we provide independent Administrative Agent, Collateral Agent, Sub-Agent, and Successor Agent services. Since 2007, SRS Acquiom has supported more than 11,500 transactions globally. By bringing efficiency, expertise, and purpose-built technology to complex financial transactions, we help sophisticated deal parties focus on building great businesses and maximizing value. We're equally committed to building careers as we are to building solutions. If you're looking for a company with entrepreneurial energy, a proven record of growth and innovation, and a culture that supports your next career move, we'd love to talk. A few benefits our employees enjoy Day‑one coverage: medical, dental, and vision plans so you're protected from the start A 401(k) with a 4% company match to keep your future on track Discretionary time off - take the time you need, when you need it Employer‑paid life insurance, with the option to add extra coverage for peace of mind Employee Assistance Programs for confidential support when life gets complicated Discounted pet insurance (because furry family members count, too) A fitness credit to back your health and wellness goals Pre‑tax plans for dependent care, transportation, and flexible spending Position Summary We are growing our Platform Engineering team and are looking to add a Data Engineer who will be able to help build our internal data platform. This data platform is central to ensuring that our software development and data analytics teams have governed access to the data they need. As a Data Engineer on the Platform Team, you will be a part of driving the design, development, and implementation of our data platforms. Your responsibilities include partnering with the architect and engineering teams in developing our platform, as well as providing best practices and approaches to ensure security, resiliency, and availability of solutions. You will be challenged to create strategies that ensure the secure consumption of the platform through continuous innovation, simplification, and self-service automation. Location: This position is fully remote within the Continental United States. In-person visits to the Denver headquarters are required, on average three times per year. Compensation: The salary range for this position is between $125k and $140k, depending on experience level. Primary Responsibilities Participate in all initiatives keeping all compliance, security and regulatory needs satisfied Be a thought leader by staying abreast of current and emerging technologies and industry trends. Work with your peers to design and build data platform capabilities and incorporate the necessary automations and tool configurations that ensure agile delivery and secure consumption Design and implement processes and tools that enable product engineering and BI/Analytics teams to consume an extensible and scalable data platform, which enforces all needed governance and consistent operational models Be a trusted advisor for initiatives by providing objective, practical, and relevant ideas, insights, and advice while also building organizational partnerships and networks to ensure comprehensive capabilities are developed with input from appropriate business and Engineering resources Directly support the use and delivery of data platform services in the organization Ensure that all data platform solutions follow established security and compliance controls Maintain existing and future software services code and configuration Make recommendations for improvements to existing architecture Help implement new technologies for future deployment Provide technical guidance, knowledge transfers, and mentorship to clients on their data platform adoption Help implement and improve the development of data pipelines Other duties as assigned Required Qualifications & Skills B.S. in Computer Science or equivalent experience 4+ years of experience with and strong working knowledge of SQL required, and understanding of data models 4+ years of experience with or strong knowledge of Postgresql, MySQL, or other relational database design and programming: stored procedures, functions, PL/pgSQL Experience with or working knowledge of managing AWS Data Infrastructure: S3, RDS, Managed Streaming Kafka, Lake Formation, Glue, Glue Data Catalog, Athena, Quicksight, DataZone Experience with systems engineering and/or software development: Java, Javascript, Python, PHP Experience with modern API platform design, security practices, and data architectures: event-driven architecture, change data capture, pub/sub Preferred Qualifications & Skills Experience with cloud solution design patterns: CI/CD, Docker, Kubernetes, containers, microservices, distributed caching Experience with Terraform scripting Experience with data integrations from third-party platforms: NetSuite, Salesforce, Workday, Pendo Experience with reporting and analytic tools: Grafana, Kibana, Elasticsearch, Open Telemetry Experience with ETL processes, projects, and tooling: Boomi Understanding of systems hardening and secure systems configuration Experience with regulatory compliance standards: HIPAA, PCI, SEC, FINRA Excellent written and verbal communication skills, with the ability to present complex technical information in a clear and concise manner to a variety of audiences Understanding of Agile/Kanban methodologies Desired Characteristics Self-motivated Intellectually curious Collaborative Amiable Operates with the highest integrity and attention to detail Passionate about efficient, scalable business processes Ability to prioritize and multitask across many projects Physical Requirements/Special Demands Must be available to work standard business hours and occasional nights/weekends. Travel is required up to three times per year ** We are unable to sponsor or take over sponsorship of employment visas. Candidates must be legally authorized to work in the United States without the need for current or future visa sponsorship to move forward in the hiring process. ** Fraud & spam screening. We use tools in our applicant tracking system to help detect potentially fraudulent or spam applications. These tools analyze limited technical and contact information (such as IP address, device/browser signals, and email/phone characteristics) to flag patterns that may indicate automated, inauthentic, or suspicious activity. Flags are used to prioritize human review and do not, by themselves, determine hiring outcomes. Learn more in our Privacy Policy. This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Duties, responsibilities, and activities may change, or new ones may be assigned at any time with or without advanced notice. With respect to its programs, services, activities, and employment practices, SRS Acquiom Inc. assesses qualified individuals without regard to their race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), age, national origin, disability, veteran status, genetic information, or other protected status. Requests for reasonable accommodation or the provision of auxiliary aids should be directed to Human Resources.

🏢 Summary: Senior Data Engineer role focused on designing and building scalable serverless data ingestion pipelines in AWS within the healthcare domain. The position emphasizes strong Python engineering, cloud architecture leadership, and implementation of modern data platforms and DevOps practices. The role involves driving technical excellence and delivering reliable, high-impact data solutions in an international environment. 🗂️ Requirements: 10+ years of experience in Python programming, Strong software engineering skills in data processing, Extensive experience with AWS Cloud and Serverless Architecture, Hands-on experience with AWS Lambda, S3, and Cognito, Experience building E2E automated tests for data pipelines, Practical knowledge of Data Mesh and Medallion Architecture, Experience with Infrastructure as Code using AWS CDK or Terraform, Experience with CI/CD pipelines using GitLab or GitHub Actions, Experience with ETL/ELT processes and dbt, Experience working with GraphQL, Minimum B2 level English proficiency 📃 Skills: Python, AWS, Lambda, S3, Cognito, Boto3, DataMesh, Medallion, CDK, Terraform, GitLab, GitHubActions, ETL, ELT, dbt, GraphQL, CI/CD 🏢 Description: We are looking for a highly skilled Data Engineer to join our client in the healthcare sector. Our requirements: Technical Expertise: Python Programming: 10+ years of experience with strong Software Engineering skills focused on data processing. AWS & Serverless: Extensive experience with AWS Cloud, specifically focusing on Serverless Architecture and services (including AWS Lambda , AWS S3 Tables , and AWS Cognito ). Automated Testing: Proven experience in developing End-to-End (E2E) automated tests to ensure pipeline reliability, utilizing tools such as Boto3 for AWS resource validation. Data Concepts: Practical knowledge of Data Mesh and Medallion Architecture , along with general data processing and analysis. DevOps & IaC: Hands-on experience with Infrastructure as Code ( AWS CDK or Terraform ) and CI/CD pipelines ( GitLab pipelines or GitHub Actions ). Modern Tooling: Experience with ETL/ELT solutions, dbt , and GraphQL . Communication & Soft Skills: English Language: Minimum B2 level , enabling smooth daily technical and business communication in a global environment. Collaboration: Excellent communication skills and the ability to thrive in a collaborative, international team. Standards: A strong commitment to high standards of ethics, quality (Clean Code), and reliable delivery. Nice to have: Experience with Snowflake and SQL . Knowledge of Data Vault 2.0 modeling. Experience with Databricks . Familiarity with the Microsoft ecosystem: C# / .Net, T-SQL, SQL Server , and Azure DevOps . Experience with Star Schema database modeling. Knowledge of Descriptive Statistics. Your responsibilites: Design and build scalable Data Ingestion pipelines within the AWS cloud ecosystem. Lead technical delivery and implementation of core platform components, ensuring architectural integrity across the entire data lifecycle. Collaborate with Engineering Managers and cross-functional teams across the globe and Poland. Drive technical excellence by improving team processes, architecture standards, and engineering best practices. Support and consult with stakeholders to ensure successful delivery of high-impact, data-driven solutions. Contribute to the growth and maturity of the team’s cloud and data engineering capabilities. We offer: Challenging role within the company that creates innovative solutions. Work in international environment on demanding projects. Remote work model. Subsidized private medical care, life insurance, multisport card. Integration meetings. Employee referral program. If you have a deep expertise in Python and AWS , and building scalable Serverless data architectures is where you truly excel, this is the perfect role for you!

🏢 Summary: Opportunity for an experienced Python Data Engineer to build scalable data platforms and analytics systems for global financial institutions. The role focuses on designing cloud-based data pipelines, enhancing system reliability, and integrating modern AI tools into engineering workflows. You will collaborate across distributed teams to deliver high-quality solutions in enterprise data, risk analytics, and AI-driven metadata applications. 🗂️ Requirements: 4+ years of professional experience in software or data engineering using Python, Strong knowledge of data architecture, data modeling, and data warehousing, Ability to write complex SQL queries and perform advanced debugging, Experience building scalable, distributed data pipelines in cloud environments, Experience with AWS, Familiarity with event-driven architectures 📃 Skills: Python, SQL, AWS, Databricks, Spark, PySpark, Scala, Delta, Airflow, dbt, Kafka, Kubernetes, MongoDB, Grafana, Loki, Prometheus, OpenTelemetry 🏢 Description: We are partnering with top-tier global financial institutions to scale their core technology and data infrastructure. We are looking for an experienced and product-oriented Python Data Engineer to join our technology group. In this role, you will work at the intersection of cutting-edge technology and institutional finance. You will collaborate closely with data consumers, engineering teams, and business stakeholders to push the firm's technological capabilities forward. What You’ll Do: Core Responsibilities: Design, develop, and deliver high-quality, scalable Python-based solutions. Drive engineering excellence by ensuring system reliability, automating processes, and maintaining high operational standards. Actively experiment with and integrate modern AI coding tools (e.g., Copilot, Cursor) to streamline engineering workflows. Lead design discussions, mentor junior colleagues, and communicate proactively across a geo-distributed team. Depending on the specific project or team, your focus may include: Enterprise Data Platforms: Building cloud-based (AWS) pipelines for data ingestion, streaming, and cataloging. Risk & Portfolio Analytics Systems: Developing software for financial data ingress/egress, performance/exposure monitoring, and automated reconciliation. AI & Metadata Applications: Extending AI-powered semantic data access layers and internal data catalogs. What We’re Looking For: Experience: 4+ years of professional experience in software or data engineering, specifically using Python . Data Skills: Solid understanding of data architecture, modeling, and warehousing. Excellent debugging acumen and comfort writing complex SQL statements. Cloud & Architecture: Experience building scalable, distributed pipelines in a cloud environment (preferably AWS ). Familiarity with event-driven architectures. Mindset: Impact-oriented, proactive, self-starting learner who embraces engineering automation, navigates ambiguity well, and holds themselves to high ethical standards. Nice to Have: Big Data & Orchestration: Expertise in Databricks, Spark (PySpark/Scala), Delta Lake, and orchestration tools like Airflow, dbt, or Kafka. Infrastructure & Observability: Working knowledge of Kubernetes, MongoDB, and observability stacks (Grafana, Loki, Prometheus, OpenTelemetry). Offer: Competitive compensation (30k - 45k PLN) with flexible contracting options (B2B/UoP). Premium office location in the heart of Warsaw. Comprehensive private medical and dental care. Sports card and wellness benefits. Private life insurance