Senior Data Engineer (cloud&ai)

🏢 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: Lead Data Engineer role focused on driving AI initiatives and building scalable cloud-based data architectures in a global environment. The position involves technical ownership of data platforms, designing secure and high-performing systems, and leading data engineering efforts in a DevOps setting. The role emphasizes AI integration, cloud infrastructure, and enterprise-grade data governance. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python in production, Strong experience designing and scaling complex data systems, Hands-on experience with AI technologies, Hands-on experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs and data integration, Experience with automation tools, Knowledge of data governance practices, Understanding of data security and compliance standards, Proven experience leading and mentoring engineers 📃 Skills: Python, SQL, AWS, Azure, Java, AI, RAG, MCP, APIs, DevOps, Automation, Monitoring, Cloud, DataEngineering, DataPipelines, Governance, Security, Compliance, Backend 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: Lead Data Engineer role focused on building scalable AI and cloud-based data solutions in a global environment. The position involves leading data engineering initiatives, designing secure cloud architectures, and supporting AI applications using AWS or Azure. The offer includes hybrid work, professional development opportunities, and a comprehensive benefits package. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python, Experience designing and scaling complex data systems, Hands-on experience with AI technologies, Experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs, data integration, automation tools, and data governance, Understanding of data security and compliance standards, Leadership and mentoring experience, English and Polish proficiency (minimum B2) 📃 Skills: Python, SQL, AWS, Azure, Java, APIs, DevOps, AI, RAG, MCP, Automation, DataGovernance 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: The role focuses on ensuring high quality across data pipelines and analytics by designing and executing tests for ETL workflows and large-scale data transformations. It involves building and maintaining automated testing solutions using Python and Spark technologies, validating data accuracy, and collaborating with Data Engineers and stakeholders. The position also includes improving QA processes, supporting integration and performance testing, and creating reporting dashboards for quality metrics. 🗂️ Requirements: Strong hands-on experience with Python for automation, scripting, and data validation, Good working knowledge of PySpark, Good working knowledge of Spark SQL, Experience with Azure Data Factory for ETL orchestration and validation, Experience with Azure Databricks for large-scale data processing, Ability to build Power BI reports for QA visibility, Experience with testing frameworks in data/analytics environments, Understanding of QA practices for data-centric systems, Ability to design and maintain automated tests for data pipelines 📃 Skills: Python, PySpark, SparkSQL, Azure, DataFactory, Databricks, PowerBI, ETL, SQL, Automation, Testing 🏢 Description: We’re looking for a QA Engineer with strong hands-on skills to help ensure high quality across our data and analytics work. In this role, you’ll design and run tests for data pipelines and ETL workflows, build and maintain automation, and work closely with Data Engineers and business stakeholders to validate data accuracy and reliability. If you enjoy digging into data, improving testing frameworks, and building practical automation that catches issues early, you’ll feel at home here. Responsibilities: Design and improve testing processes for data pipelines, ETL workflows, and analytics outputs. Develop and maintain automated tests and utilities in Python, PySpark, and Spark SQL to validate transformations and data integrity. Execute and report on test cases for new features and bug fixes using appropriate tools and frameworks. Collaborate with Data Engineers to validate end-to-end pipelines and support delivery during busy periods (including occasional help with data transformations). Apply best practices across test automation, integration testing, performance testing, and manual testing where needed. Write clean, scalable test code that’s easy to maintain and provides good coverage. Create and maintain documentation for test processes, test cases, and results. Work with stakeholders to understand testing needs and recommend practical solutions aligned with business goals. Build simple Power BI dashboards to track test results and quality metrics. Requirements: Strong hands-on experience with Python (automation, scripting, data validation). Good working knowledge of PySpark and Spark SQL , especially for testing large-scale data transformations. Experience with Azure Data Factory for orchestrating and validating ETL workflows. Experience with Azure Databricks for data processing and testing data pipelines at scale. Ability to build simple Power BI reports for visibility into QA results. Familiarity with testing frameworks and approaches used in data/analytics environments. Understanding of QA practices for data-centric systems (automation, integration, performance testing, data quality checks). Strong communication skills and a collaborative mindset.

🏢 Summary: Full-time Data Engineer role focused on building and maintaining scalable, cloud-based data platforms for large-scale analytics. The position involves developing end-to-end data pipelines and collaborating with researchers and data professionals to enable data exploration and insight generation. 🗂️ Requirements: 3+ years experience in Data Engineering or similar role, Strong Python skills, Experience with Spark or Scala, Experience with big data technologies, Ability to design scalable data solutions 📃 Skills: Python, Spark, Scala, Cloud, ETL, BigData, GraphDB 🏢 Description: Role Title: Data Engineer Location: Warsaw, hybrid Contract Type: Umowa o Pracę - full time employment 25000-36000 zl gross/month - negotiable (depending on years of experience) A growing research-focused team is looking for a Data Engineer to build and support data platforms that help professionals extract insights from large, complex datasets. This role involves working closely with researchers, analysts, and data scientists to design scalable data solutions. What you’ll do Build and maintain end-to-end data pipelines (ingestion, transformation, delivery) Develop cloud-based data infrastructure for large-scale analytics Work with teams to enable data exploration and visualization Evaluate and prototype new big data technologies Requirements 3+ years experience in Data Engineering or similar role Strong Python development skills Experience with Spark or Scala Interest or experience in big data technologies Ability to design innovative solutions to data challenges Nice to have • Experience with graph databases

🏢 Summary: Hands-on data infrastructure engineering role focused on large-scale pipeline migrations and evolution of the company’s data processing stack. The position involves contributing to platform development across Flink and Lakehouse architectures while ensuring performance, reliability, and cost efficiency. High-impact role embedded in a data engineering team delivering production-grade data platforms. 🗂️ Requirements: Strong Java development experience, Experience with JVM-based data processing framework, Experience with Flink, Beam, Dataflow or Spark, Proficiency in SQL, Experience with BigQuery, Experience with cloud infrastructure, Experience with containerized applications, Knowledge of Kubernetes basics, Experience with Scala or Python for data pipelines, Experience working with production data engineering systems 📃 Skills: Java, Flink, Beam, Dataflow, Spark, SQL, BigQuery, Kubernetes, Scala, Python, JVM, DevOps, Lakehouse, Cloud 🏢 Description: The Data Infrastructure PA enables the company to solve complex and critical data engineering problems by providing platforms and tooling for the production, management, and consumption of high-quality data. We're looking for an engineer to support hands-on implementation and migration work as we evolve our data processing stack. This is a high impact and execution-focused engagement — you'll be contributing to company wide migration efforts and platform development. What You'll Work On You'll be embedded in a team in Data Infrastructure PA, contributing to hands-on engineering work. This includes large-scale pipeline migrations — validating performance and cost outcomes and helping move workloads to our evolving stack — as well as contributing to platform development across our Flink platform, Lakehouse architecture and beyond, as our priorities evolve. What We're Looking For You have solid, hands-on experience in backend engineering and are comfortable jumping into an existing platform codebase and making meaningful contributions quickly. Specifically: Strong Java development skills, with experience in data platform or data engineering contexts Practical experience with at least one JVM-based data processing framework — Flink experience is a plus; Beam, Dataflow, or Spark also relevant Comfortable with SQL and cloud data analytics platforms, particularly BigQuery DevOps is part of your day-to-day: you work with cloud infrastructure, containerised applications, and are familiar with Kubernetes basics Experience working with data engineering pipelines in Scala and/or Python You write quality code and understand what it means to ship reliably in a production environment You can work autonomously in an ambiguous environment and move quickly without waiting to be directed Nice to Have Prior experience with large-scale pipeline migrations Familiarity with cost optimisation in cloud data processing workloads Job Posting Start Date:   2026-05-18 Job Posting End Date:   2026-11-27

🏢 Summary: Hands-on Observability Engineer role focused on building and automating enterprise-grade monitoring and observability solutions across AWS-based cloud and distributed systems. The position centers on developing infrastructure as code, CI/CD pipelines, and monitoring ecosystems to improve reliability, performance, and incident response. Approximately 90% of the role involves coding in Python and Terraform. 🗂️ Requirements: Strong hands-on experience with AWS, Strong Python development and scripting experience, Strong experience with Terraform, Experience building and maintaining CI/CD pipelines using Jenkins, Experience with Elasticsearch and ELK Stack, Experience with Linux systems, Shell scripting skills, Understanding of monitoring, logging, and alerting concepts, Experience working in Agile or DevOps environments 📃 Skills: AWS, Python, Terraform, Jenkins, Elasticsearch, ELK, Linux, Bash, CI/CD, Kubernetes, Grafana, Prometheus, Datadog, NewRelic, Snowflake, Databricks, dbt, Matillion 🏢 Description: Role Overview We are looking for a skilled and proactive Observability Engineer to implement, automate, and support enterprise-grade observability and monitoring solutions across cloud and application platforms. The ideal candidate should have strong AWS infrastructure knowledge, hands-on automation skills, and experience building reliable monitoring and alerting ecosystems for modern distributed applications. The role involves working closely with Platform Engineering, Data Engineering, and Application teams to develop observability solutions and bring operational visibility, reliability, incident detection, and platform performance. Main Responsibilities ·        Design, implement, and maintain observability solutions for cloud-native and distributed systems. ·        Build monitoring, logging, alerting, and dashboarding solutions across infrastructure and applications. ·        Develop automation scripts and tooling using Python. ·        Implement and maintain Infrastructure as Code (IaC) using Terraform. ·        Build and support CI/CD pipelines using Jenkins and Git-based workflows. ·        Configure and optimize monitoring for AWS services, Kubernetes workloads, APIs, databases, and applications. ·        Create actionable alerts and operational dashboards to improve incident response and system reliability. ·        Work with engineering teams to onboard applications into observability platforms. ·        Support troubleshooting, root cause analysis, and performance optimization initiatives. ·        Ensure observability standards, governance, and best practices are followed across projects. Key Requirements ·        Strong hands-on experience with Amazon Web Services (AWS). ·        Solid Python development/scripting experience. ·        Strong experience with Terraform. ·        Experience building and maintaining CI/CD pipelines using Jenkins. ·        Elasticsearch / ELK Stack experience and building queries. ·        Worked with Data Platforms monitoring is preferred. ·        Experience with Linux systems and shell scripting. ·        Understanding of monitoring, logging, and alerting concepts. ·        Experience working in Agile/DevOps environments. Nice to Have Skills Experience with any of the following is highly desirable: ·        Snowflake ·        Databricks ·        dbt ·        Matillion ·        Grafana ·        New Relic ·        Datadog ·        Prometheus ·        Elasticsearch / ELK Stack experience NOTES: We are looking for an Engineer who loves to build. This is a highly technical role—90% of the job is hands-on coding in python and terraform.

🏢 Summary: Senior individual contributor role leading enterprise-scale data architecture and real-time partner integrations, owning the design of scalable batch and streaming pipelines across systems. Responsible for building and operating the data plane behind a strategic DFM AI + IQE integration, enabling low-latency, bidirectional data flows between platforms. Sets engineering standards for data modeling, CI/CD, governance, and observability while collaborating cross-functionally. 🗂️ Requirements: Bachelor's degree in STEM or equivalent experience, 5+ years in data engineering with ownership of large-scale data systems, Deep expertise in Snowflake and cloud data warehouses, Expert-level SQL, Strong Python proficiency, Experience building and optimizing modern data pipelines (dbt, Airbyte, Airflow or similar), Experience designing enterprise data architecture across multiple systems and partner boundaries, Knowledge of batch and stream processing systems, Experience with highly scalable data stores, Experience writing database-heavy services or APIs, Strong understanding of CI/CD, automated testing, contract testing, schema evolution, Strong knowledge of AWS and cloud-native infrastructure, Enterprise or partner system integration experience (PLM, ERP, or SaaS), Experience with infrastructure as code frameworks, Experience with event-driven architectures and CDC pipelines 📃 Skills: Snowflake, SQL, Python, dbt, Airbyte, Airflow, Kafka, Spark, Kinesis, Apache, Iceberg, AWS, Terraform, CloudFormation, Teamcenter, BMIDE, APIs, CI/CD, CDC, Looker, Streamlit 🏢 Description: Xometry is looking for a Staff Data Engineer to join the Data Platform team. This is a senior individual contributor role with broad technical scope and high organizational impact. You will own data architecture decisions, lead the design of scalable pipelines and platforms, and set the engineering bar for how data systems are built and operated. A defining piece of this role is owning the data architecture behind the DFM AI + IQE integration with a strategic partner. You will serve as the data engineering lead for the digital thread connecting the platform to partner ecosystems including Solid Edge, NX, Designcenter, and Teamcenter, building pipelines, data contracts, and observability to move quotes, parts, manufacturability signals, and pricing data in real time. Responsibilities - Lead the design and implementation of enterprise-scale data architecture and engineering solutions across multiple systems and domains - Architect and build the data layer for embedded DFM AI + IQE integrations, including bidirectional pipelines and joint data models for parts, BOMs, quotes, and manufacturability signals - Design low-latency signal paths delivering DFM and pricing feedback into designer environments - Establish governance, lineage, and audit capabilities for partner-integrated data systems - Architect and optimize reliable batch and streaming pipelines for complex, high-volume, event-driven data flows - Own the full lifecycle of data engineering work from ingestion and transformation to delivery and observability - Define and enforce best practices for data modeling, CI/CD, testing, code quality, contract testing, and schema evolution - Solve complex cross-domain technical challenges aligned with business objectives - Develop multi-quarter technical roadmaps and execution plans - Collaborate with engineering, product, data science, business stakeholders, and partner engineering teams - Mentor engineers through design and code reviews - Evaluate and recommend tools, platforms, and architectural patterns Qualifications - Bachelor's degree in a STEM field (or equivalent experience) - At least 5 years of experience in data engineering with ownership of complex, large-scale systems - Deep expertise in Snowflake, including optimization and performance tuning - Expert-level SQL and strong Python proficiency - Experience with modern data tooling such as dbt, Airbyte, and Airflow - Experience designing enterprise data architectures spanning multiple systems and partner boundaries - Knowledge of batch and stream processing technologies (e.g., Kafka, Spark, Kinesis) and scalable data stores (e.g., Apache Iceberg) - Experience building database-heavy services or APIs with focus on testability and maintainability - Strong understanding of CI/CD, automated testing, contract testing, and schema evolution in data pipelines - Strong knowledge of AWS and cloud-native infrastructure - Enterprise integration experience with PLM, ERP, or large SaaS systems; Teamcenter experience is a strong plus - Familiarity with data visualization tools such as Looker or Streamlit - Experience with data governance, data quality frameworks, and observability tooling - Exposure to lakehouse or data mesh architectures - Experience with infrastructure as code (Terraform, CloudFormation) - Experience with event-driven architectures, CDC pipelines, and low-latency operational data flows Benefits - Estimated base salary range: $180,000–$200,000 annually plus commission, depending on experience and location - Competitive benefits package including 401(k) match - Medical, dental, and vision insurance - Life and disability insurance - Generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave - Employee assistance and wellbeing resources

🏢 Summary: Senior individual contributor role responsible for designing and owning enterprise-scale data architecture and real-time data pipelines that power a strategic DFM AI + IQE partner integration. The position focuses on building scalable batch and streaming systems, defining data models, and ensuring governance, observability, and CI/CD standards across cross-system integrations. The engineer leads the digital data plane connecting internal platforms with external PLM ecosystems in a high-impact, cloud-native environment. 🗂️ Requirements: Bachelor’s degree in STEM or equivalent experience, Minimum 5 years of experience in data engineering, Deep expertise in Snowflake or similar cloud data warehouse, Expert-level SQL, Strong Python proficiency, Hands-on experience with modern data pipeline tools (dbt, Airbyte, Airflow or similar), Experience designing enterprise data architecture across multiple systems, Knowledge of batch and stream processing systems, Experience with highly scalable data stores, Experience with CI/CD, automated testing, contract testing, schema evolution, Strong knowledge of AWS data ecosystem, Experience integrating with enterprise or partner systems (e.g., PLM, ERP, SaaS) 📃 Skills: Snowflake, SQL, Python, dbt, Airbyte, Airflow, Kafka, Spark, Kinesis, Apache, Iceberg, AWS, Teamcenter, BMIDE, APIs, Looker, Streamlit, Terraform, CloudFormation, CI/CD, CDC 🏢 Description: Xometry is looking for a Staff Data Engineer to join the Data Platform team. This is a senior individual contributor role with broad technical scope and high organizational impact. You will own data architecture decisions, lead the design of scalable pipelines and platforms, and set the engineering bar for how data systems are built and operated. A defining piece of this role is owning the data architecture behind the DFM AI + IQE integration with a strategic partner. You will serve as the data engineering lead for the digital thread connecting the platform to partner ecosystems including Solid Edge, NX, Designcenter, and Teamcenter. You will build the pipelines, contracts, and observability that move quotes, parts, manufacturability signals, and pricing between systems in real time. Responsibilities Lead with technical depth – Design and drive the implementation of enterprise-scale data architecture and engineering solutions spanning multiple systems and domains. Own the partner integration data plane – Architect and build the data layer of the embedded DFM AI + IQE integration with Teamcenter and Designcenter. Own bidirectional pipelines, the joint data model for parts, BOMs, quotes, and manufacturability signals, low-latency feedback paths, and required governance, lineage, and audit controls. Build for scale – Architect and optimize reliable batch and streaming data pipelines, data models, and platforms handling complex, high-volume and event-driven data flows. Own the full lifecycle – Take end-to-end accountability from data acquisition and transformation through delivery, observability, and performance. Set the standard – Define and enforce best practices for data modeling, CI/CD, testing, code quality, contract testing, and schema evolution. Solve ambiguous problems – Navigate cross-domain technical challenges and deliver solutions meeting business and technical objectives. Develop multi-quarter roadmaps – Translate strategic priorities into technical plans and timelines. Collaborate broadly – Partner with engineering, product, data science, business stakeholders, and external partner engineering teams. Mentor and elevate – Guide engineers through design reviews, code reviews, and mentorship. Evaluate and adopt – Recommend tools, platforms, and architectural patterns within the data engineering ecosystem. Qualifications Bachelor's degree in a STEM field (or equivalent experience) and at least 5 years of experience in data engineering with ownership of large-scale data systems. Deep expertise with cloud data warehouses, preferably Snowflake, including optimization and performance tuning. Expert-level SQL and strong Python proficiency. Experience building and optimizing data pipelines and architectures using tools such as dbt, Airbyte, or Airflow. Experience planning and implementing enterprise data architecture across multiple systems and organizational boundaries. Working knowledge of queueing, batch and stream processing (Kafka, Spark, Kinesis) and scalable data stores (Apache Iceberg). Experience developing database-heavy services or APIs with focus on testability and maintainability. Strong understanding of CI/CD, automated testing, contract testing, and schema evolution in data pipelines. Strong knowledge of AWS data ecosystem and cloud-native infrastructure. Enterprise integration experience with PLM, ERP, or large SaaS systems; Teamcenter experience is a strong plus. Familiarity with data visualization tools such as Looker or Streamlit. Experience with data governance, data quality frameworks, and observability tooling. Exposure to lakehouse or data mesh architectures. Experience with infrastructure as code frameworks such as Terraform or CloudFormation. Experience with event-driven architecture, CDC pipelines, and low-latency operational data flows. Benefits Base salary range: $180,000–$200,000 annually plus commission, depending on experience and location. Competitive benefits package including 401(k) match, medical, dental, and vision insurance; life and disability insurance; generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave; employee assistance program and additional wellbeing resources.

🏢 Summary: 100% remote B2B contract role for an Expert Data Engineer to build and maintain distributed Big Data pipelines supporting decision-making processes in an international banking environment. The position focuses on near real-time processing of large-scale structured and unstructured data using Spark, Scala, and Hadoop ecosystems. It requires strong experience in data engineering, automation, and continuous delivery on Big Data platforms. 🗂️ Requirements: Minimum 5 years of experience with Spark and Scala, Minimum 7 years of experience with Python, Minimum 5 years of experience with Linux, Experience with distributed data processing engines, Experience with Hadoop ecosystem (Hive, Oozie, MapReduce), Strong SQL skills, Experience in building data flows, Expert-level knowledge of Git and BitBucket, Experience with unit testing (JUnit 5, Mockito, Spark testing), Advanced English (min. B2) 📃 Skills: Scala, Python, Spark, Hadoop, Hive, Oozie, MapReduce, SQL, Linux, Git, BitBucket, JUnit, Mockito 🏢 Description: Workplace: 100% remote Notice period: 30 days Form of cooperation: B2B contract with ITFS Rate: 140 - 170 PLN/h + VAT Client: international bank Technology stack: Scala, Python, Spark Language: English (min. B2) The recruitment process: screening call with ITFS (~ 20 minues)  → technical interview with the client → decision As an Expert Data Engineer you will be building data systems to support decision making processes. You will focus on developing and maintaining data pipelines. This position will require advanced technical depth and experience, and communication skills. What you’ll be doing: Building a distributed and highly parallelized Big data processing pipeline which process massive amount of data (both structured and unstructured data) in near real-time Leveraging Spark and Scala to transform corporate data to enable data products to be built Continuous delivery on Hadoop and other Big Data Platforms Automating processes where possible and are repeatable and reliable Must have: Experience in Spark & Scala - minimum 5 years; Experience in Python - minimum 7 years Experience in Linux - minimum 5 years Experience with distributed data processing engines like Spark Experience with Hadoop and (Hive, Oozie, map reduce, etc) Strong/Advanced SQL skills Previous experience in creating data flows BitBucket and GIT expert Unit testing Experience (Junit 5, Mockito, Spark testing) Advanced English skills Must-have knowledge and experience: To succeed in this role, we believe that you: Are collaborative and achieve your expectations through communication and teamwork Are curious, responsive and can understand the needs of others to ensure delivery of the desired results Work qualitatively and strive to always do things better Have a high level of self-motivation Nice to have: Code versioning strategy & Branching strategy Familiar with Agile/Safe framework We offer: Transparent terms of cooperation with a company with a secure and stable market position and opportunities for growth. Access to benefits (co-financing for Enel-med and Multisport healthcare packages, and a basic accounting package – free for up to three invoices per month).