Senior Data Engineer - Databricks

🏢 Summary: Senior hands-on Staff Data Engineer role focused on owning and scaling a centralized Databricks lakehouse platform, building production-grade data pipelines, and leading multi-terabyte legacy migrations. The position combines architectural decision-making, data modeling, and technical leadership to support multiple financial product lines. The engineer acts as the primary onshore technical authority ensuring platform reliability, governance, and scalability. 🗂️ Requirements: 5+ years of data engineering experience, Strong proficiency in SQL, Python, and PySpark, Hands-on experience with Databricks (Delta Lake, Workflows, Databricks SQL), Experience building and maintaining large-scale ETL/ELT pipelines, Proven ability to design scalable data models, Experience migrating multi-terabyte datasets, AWS experience (S3, IAM, VPC), Infrastructure-as-code experience (Terraform preferred), Knowledge of data governance, lineage, and access controls, Ability to make independent architectural decisions, Experience mentoring data engineers, US-based with timezone overlap for stakeholder collaboration 📃 Skills: SQL, Python, PySpark, Databricks, Delta, Lake, Workflows, DatabricksSQL, UnityCatalog, AWS, S3, IAM, VPC, Terraform, ETL, ELT, DataModeling, Governance 🏢 Description: Exactera has offices in New York City, Tarrytown NY, San Diego, CA, London, and Argentina. The Role As Staff Data Engineer, you will provide senior onshore technical leadership for the data engineering team. You will own a defined slice of our centralized Databricks data platform with full accountability for decisions and delivery, serve as a technical counterpart to the Principal Data Platform Engineer, and drive architectural judgment and independent problem-solving as platform complexity scales post-migration. This is a hands-on data engineering role focused on building and maintaining production pipelines, exercising architectural judgment on data modeling and pipeline design, and serving as the onshore escalation point and institutional knowledge backup for platform decisions. The Business Challenge We operate multiple product lines (Transfer Pricing, R&D Services, RoyaltyStat, Provisioning), each with distinct databases containing enterprise financial data—journal entries, general ledgers, and financial statements. Our immediate challenge is migrating multi-terabyte datasets from legacy systems to a unified Databricks lakehouse while establishing governance patterns that enable multi-product operations at scale. As the platform matures, the data engineering team needs senior onshore technical presence to drive architecture ownership and maintain platform quality. What You'll Build Production Data Pipelines: Build and maintain production data pipelines within the patterns and governance established by the Lead Data Platform Engineer, ensuring reliability and performance at multi-terabyte scale. Data Modeling & Architecture: Exercise architectural judgment on data modeling, pipeline design, and platform usage—translating complex business requirements into scalable data solutions across our product portfolio. Stakeholder Engagement: Engage proactively with product and engineering stakeholders to translate requirements into data solutions, serving as the primary onshore technical point of contact for data engineering needs. Platform Quality: Drive platform quality through code reviews, testing practices, and engineering standards that ensure the team delivers reliable, maintainable data infrastructure. Knowledge & Continuity: Serve as onshore escalation point and institutional knowledge backup for platform decisions, reducing single-point-of-failure risk and building onshore technical depth as the platform scales. Business Problems You'll Solve Multi-Product Data Delivery: Implement data pipelines that serve multiple product lines (Transfer Pricing, R&D Services, RoyaltyStat, Provisioning) with distinct data requirements, ensuring each product gets the data it needs reliably and on schedule. Legacy Migration Execution: Lead pipeline implementation for migrating multi-terabyte datasets from legacy systems to Databricks, working within the architecture defined by the Lead Data Platform Engineer. Onshore Technical Leadership: Provide the senior judgment layer the current nearshore team cannot—owning problems end-to-end, making independent architectural decisions, and mentoring engineers to raise the quality bar across the team. Cross-Team Coordination: Bridge the gap between product teams and data infrastructure, translating business requirements into data solutions and ensuring the data platform delivers on product commitments. Required Experience Core Data Engineering SQL, Python, and PySpark—production pipeline implementation and performance optimization Databricks experience—Delta Lake, Workflows, and Databricks SQL; Unity Catalog familiarity preferred 5+ years in data engineering with demonstrated ability to own problems end-to-end without close direction Experience building and maintaining ETL/ELT pipelines at scale, including error handling, monitoring, and data quality validation Strong data modeling skills across structured and semi-structured data sources Platform & Infrastructure AWS experience (S3, IAM, VPC) with ability to collaborate on infrastructure decisions Infrastructure-as-code experience (Terraform preferred) Familiarity with data governance patterns (Unity Catalog, data lineage, access controls) Technical Leadership Demonstrated ability to exercise independent architectural judgment—not just ticket execution Experience mentoring or guiding junior and mid-level data engineers Strong written and verbal communication—able to document architecture decisions and engage directly with both technical and business stakeholders Onshore (US-based)—role requires timezone overlap, async-light communication, and direct stakeholder engagement Preferred But Not Required Experience with financial data, accounting systems (NetSuite), or enterprise ERP platforms Background building pipelines that serve AI/ML workloads (preparing data for downstream ML consumption, RAG, and LLMs) Familiarity with data governance frameworks and compliance requirements for regulated industries Experience working alongside or transitioning from nearshore engineering teams What We Offer (The following only applies to US-based positions) A collaborative team culture with opportunities for career development. Ample opportunities to be recognized, build valuable skills, and grow your career. Generous vacation policy, including paid parental leave. Comprehensive health plans with FSA and HSA options. 401(k) retirement plan. Life and disability insurance coverage. Supplemental benefits like a dependent care savings plan, pet insurance, will preparation, and an employee assistance program. About Us At Exactera, a FinTech SaaS start-up founded in 2016, we stand at the intersection of human and machine intelligence. Our corporate tax solutions are powered by AI and cloud-based technologies, serving customers worldwide. We are committed to diversity, inclusion, and equal opportunities for all. What We Offer:(The following only applies to US-based positions) A collaborative team culture with opportunities for career development. Ample opportunities to be recognized, build valuable skills, and grow your career. Generous vacation policy, including paid parental leave. Comprehensive health plans with FSA and HSA options. 401(k) retirement plan. Life and disability insurance coverage. Supplemental benefits like a dependent care savings plan, pet insurance, will preparation, and an employee assistance program. About Us: At Exactera, a FinTech SaaS start-up founded in 2016, we stand at the intersection of human and machine intelligence. Our corporate tax solutions are powered by AI and cloud-based technologies, serving customers worldwide. With over $100 million in funding from Savant Venture Fund and Insight Partners, we are poised for growth. We are committed to diversity, inclusion, and equal opportunities for all.

🏢 Summary: The role focuses on engineering production-grade AI features such as semantic search, intelligent assistants, and automated risk scoring by integrating external AI providers into a cybersecurity platform. You will own the full lifecycle from prototype to reliable, monitored production services, primarily using Python on Azure. This position emphasizes robust software engineering practices over model research or training. 🗂️ Requirements: 5+ years of professional experience building production systems in Python, Expert-level Python programming skills, Experience with ML frameworks and libraries, Proven experience shipping and operating production software, Experience integrating external APIs in production environments, Strong understanding of software architecture and design patterns, Practical experience with CI/CD pipelines, Experience with containerization using Docker, Understanding of AI/ML concepts including LLMs, embeddings, prompt engineering, RAG, Experience with unit and integration testing and mocking external services, Polish work permit, Professional proficiency in English 📃 Skills: Python, Azure, OpenAI, Anthropic, Docker, CI/CD, FastAPI, Flask, Pinecone, Weaviate, Chroma, .NET, REST, LLMs, RAG, Embeddings, Git 🏢 Description: About the Role As a Senior AI/ML Engineer you will be building AI-powered features at the core of our platform - semantic search, an intelligent assistant, automated risk scoring, and much more. We mainly integrate with external AI model providers (OpenAI, Anthropic, and others) to deliver these capabilities to our clients and internal users. This is a software engineering role, not a data science or ML research position. You won't train models from scratch or publish papers. You'll engineer production-grade systems that leverage AI to solve real cybersecurity problems. If you're a strong Python engineer excited about making AI reliable, maintainable, and impactful — we want to talk to you. We're looking for a versatile software engineer to own the journey from AI prototype to reliable production system: building robust integrations with external AI providers or fine-tuned models, standardizing our Python codebase and release pipelines, and ensuring our AI features meet the same quality bar as any other production service. What You'll Do Ship AI features end to end - from integration design through deployment to monitoring in production. You'll build and maintain Python services that power our AI-driven capabilities on Azure. Build production-ready AI / ML integrations - including retry logic, rate limiting, cost monitoring, fallback strategies, and abstraction layers that allow us to swap between model providers. Turn proofs of concept into reliable software - collaborate with our AI experts (who bring deep ML/data science expertise) to take working prototypes and engineer them into tested, documented, production-grade services. Standardize Python engineering practices - establish and evolve our code organization, testing strategies, CI/CD pipelines, and release processes for AI/ML features. Design clean service boundaries - our platform's core runs on .NET. You'll design and maintain well-defined APIs between the Python/AI layer and the rest of the system. Mentor and share knowledge - help other engineers grow their Python and AI engineering skills, and bring engineering rigor to the broader team's practices. Contribute to architecture decisions - work closely with other teams to shape how AI capabilities evolve within the platform. Who You Are Must-Haves 🔹 5+ years of professional experience building and maintaining production systems in Python. 🔹 Expert-level programming skills and proficiency with common ML frameworks and libraries. 🔹 A track record of shipping and operating software - not just building prototypes. You care about testing, monitoring, logging, and what happens after deployment. 🔹 Experience integrating external APIs in production systems - ideally AI/LLM provider APIs (OpenAI, Anthropic, Azure AI, or similar), but strong API integration experience in any domain counts. 🔹 Solid understanding of software architecture and design patterns - you have opinions about how to structure a Python project and can articulate trade-offs. 🔹 Practical knowledge of CI/CD pipelines, containerization (Docker), and modern development workflows. 🔹 A working understanding of AI/ML concepts - you know what embeddings, LLMs, prompt engineering, and RAG are. You can have a productive technical conversation with an ML specialist without needing every concept explained from scratch. 🔹 Experience with testing strategies - unit tests, integration tests, mocking external services. You instinctively write tests, not as an afterthought. 🔹 A highly adaptable, problem-solving and result-oriented mindset with the ability to work independently and take ownership in a fast-paced and dynamic environment. 🔹 Strong communication skills in English (our working language). 🔹 Willingness to learn continuously and share what you learn with the team. Nice-to-Haves 🔸 Experience with Azure cloud services. 🔸 Familiarity with FastAPI, Flask, or similar Python web frameworks. 🔸 Exposure to MLOps tooling or practices (model versioning, experiment tracking, feature stores). 🔸 Hands-on experience in Document AI / Intelligent Document Processing using traditional models and Generative AI. 🔸 Experience with vector databases (Pinecone, Weaviate, Chroma, or similar). 🔸 Any experience with .NET - helpful for cross-team collaboration, but not expected. 🔸 Background in or interest in cybersecurity as a domain. Your Team You'll join our Synapse team - a small, focused group responsible for all AI/ML capabilities within our platform. The team consists of: -> Team Leader / Architect - provides high-level architectural guidance, planning, and cross-stack delivery. -> AI / ML Engineer - deep expertise in AI/ML and data science. Your counterbalance: they bring the ML knowledge, you bring the production engineering discipline. -> AI / Backend Engineer - transitioning from .NET into AI engineering. A teammate you'll mentor and grow alongside. -> You - the person who bridges the gap between AI prototypes and reliable production systems. Why Join Us? / What We Offer: Competitive salary and benefits package. Remote work options and flexible working hours. Actual impact on the choice and shape of solutions developed. Opportunities for professional growth and development. Training and conference budget. A collaborative, innovative work environment with an iterative agile approach. The role is for six months with the possibility of extension. Both full-time and part-time cooperation are possible.

🏢 Summary: Senior Software Engineer I role focused on building and maintaining large-scale data platforms, pipelines, and AI-driven applications for measurement and reporting in the AdTech space. The position involves developing scalable batch and real-time data systems, backend services, and agentic AI solutions, with occasional frontend work. On-site presence is required four days per week in Manhattan or Denver. 🗂️ Requirements: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field, 5+ years of experience in data engineering or software engineering, Experience building large-scale data pipelines and distributed data processing systems, Proficiency in Python, Proficiency in SQL, Experience with modern data platforms (e.g., Spark, Kafka, Airflow, Snowflake, BigQuery, Redshift, Databricks), Strong knowledge of data modeling, data warehousing, and data lakes, Experience with relational and NoSQL databases, Experience with AWS, Azure, or Google Cloud, Experience building APIs and backend services, Familiarity with Generative AI, AI agents, RAG, and orchestration frameworks, Experience writing unit, integration, and end-to-end tests, Ability to work on-site 4 days per week in Manhattan or Denver 📃 Skills: Python, SQL, Spark, Kafka, Airflow, Snowflake, BigQuery, Redshift, Databricks, MySQL, PostgreSQL, MongoDB, Redis, AWS, Azure, GCP, APIs, LLM, RAG, LangChain, LangGraph, CrewAI, AutoGen, Scala, PySpark, Docker, Kubernetes, React, TypeScript, CI/CD 🏢 Description: We require people to be on-site, 4 days/week at our Manhattan or Denver office and are unable to offer relocation support. The Opportunity We are seeking a talented and motivated Senior Software Engineer I to join our Measurement and Reporting team. We enable our clients to visualize their impact across different verticals and empower our sales and operations teams to identify opportunities and address shortcomings during campaign execution. You will primarily work on designing, building, and maintaining large-scale data platforms and pipelines that power our reporting, analytics, and AI-driven solutions. In addition, you will contribute to the development of agentic AI applications that help automate workflows, enhance operational efficiency, and unlock new capabilities across the organization. You may also occasionally work on frontend applications to deliver end-to-end solutions and improve user experiences. If you're passionate about building scalable data systems, leveraging AI technologies, and solving complex engineering challenges at scale in the AdTech space, we want to hear from you! Responsibilities - Design, develop, deploy, and maintain scalable data pipelines, ETL/ELT workflows, and data processing systems supporting reporting, analytics, and machine learning applications. - Build and optimize batch and real-time data processing solutions using modern data engineering frameworks and cloud-native technologies. - Design and maintain data models, data warehouses, and data lakes to ensure high-quality, reliable, and accessible data. - Develop and maintain integrations between internal and external systems, ensuring efficient data movement and consistency across platforms. - Build and enhance agentic AI applications, leveraging LLMs, AI agents, orchestration frameworks, and automation workflows to solve business problems. - Collaborate with product, analytics, operations, and engineering teams to identify opportunities for AI-driven automation and intelligent decision-making. - Contribute to backend services and APIs that support data products, reporting applications, and AI-powered solutions. - Occasionally develop frontend features and dashboards to support internal and customer-facing applications. - Monitor, troubleshoot, and optimize data pipelines and AI systems for performance, reliability, scalability, and cost efficiency. - Implement data governance, quality checks, monitoring, and observability practices to ensure data integrity and operational excellence. - Conduct code reviews, provide constructive feedback, and promote engineering best practices across the team. - Mentor junior engineers and contribute to a culture of continuous learning, innovation, and technical excellence. Requirements - Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. - 5+ years of experience in data engineering, software engineering, or a related technical role. - Strong experience building and maintaining large-scale data pipelines and distributed data processing systems. - Proficiency in Python and SQL, with experience developing production-grade data solutions. - Experience with modern data platforms and technologies such as Databricks, Spark, Kafka, Airflow, Snowflake, BigQuery, Redshift, or similar technologies. - Strong understanding of data modeling, data warehousing, and data lake architectures. - Experience working with relational and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Redis). - Familiarity with cloud platforms such as AWS, Azure, or Google Cloud. - Experience building APIs and backend services. - Familiarity with Generative AI technologies, AI agents, prompt engineering, retrieval-augmented generation (RAG), and agent orchestration frameworks. - Demonstrated ability to stay current with AI tools and emerging technologies, with a strong aptitude for rapidly learning and adopting new frameworks. - Proficiency in writing and maintaining unit tests, integration tests, and end-to-end tests. - Ability to coordinate and work with contractors/consultants while owning the quality and delivery of assigned projects. Preferred - Familiarity with AdTech. - Experience with Scala/PySpark and distributed data processing frameworks. - Experience with Databricks and Delta Lake. - Experience building production AI applications using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar technologies. - Familiarity with vector databases and semantic search technologies. - Experience with CI/CD pipelines and Infrastructure as Code. - Familiarity with containerization (Docker) and orchestration platforms (Kubernetes). - Exposure to frontend technologies such as React, TypeScript, or modern JavaScript frameworks. - Experience with observability, monitoring, and data quality tools. Compensation Range: $135K - $220K Additional Compensation Details: Tier 2 (all other U.S. Locations outside of Tier 1) Salary: $135K – $192.5K Bonus: Offers Bonus Tier 1 (San Francisco Bay Area, New York City Tri-State Area, Los Angeles Metro Area, and Seattle Metro Area) Salary: $154K – $220K Bonus: Offers Bonus

🏢 Summary: Senior Test Engineer role focused on ensuring end-to-end quality and data accuracy across a multi-tenant SaaS analytics platform. The position covers testing of React/ASP.NET frontends, Python/C# microservices, REST APIs, and AI-driven workflows, with ownership of QA processes for monthly releases. The goal is to guarantee reliable insights and proper data isolation for enterprise clients. 🗂️ Requirements: 5+ years manual QA experience in multi-tenant SaaS platforms, Strong SQL skills for data validation, Experience testing REST APIs, Hands-on use of Postman or similar API tools, Experience with browser developer tools, Network tracing and log analysis skills, Experience with enterprise-level applications 📃 Skills: SQL, REST, Postman, React, ASP.NET, Python, C#, APIs, PowerBI, Superset, Azure, SaaS, AI, ML 🏢 Description: We're hiring a Senior Test Engineer. Our consumer intelligence platform synthesizes massive-scale data to deliver actionable insights for leading global brands such as Coca-Cola, Unilever, and Bayer. The Role As our Senior Test Engineer, you'll ensure enterprise clients receive accurate, reliable analytics through our core products spanning brand tracking, trend detection, and AI-powered innovation discovery. What You'll Own End-to-end quality across our React/ASP. net frontend, Python/C# microservices, and REST APIs Multi-tenant validation ensuring data isolation between enterprise clients and their customized AI model configurations Data accuracy testing using SQL queries to validate that insights from our ML models appear correctly in Power BI and Superset dashboards AI workflow validation testing our copilot's natural language responses Release ownership managing QA processes for monthly releases Your Background Must-haves: 5+ years of manual QA experience in multi-tenant SaaS platforms SQL skills for data validation and accuracy testing Hands-on experience testing REST APIs with Postman or similar tools Experience with browser developer tools, network tracing, and log analysis Track record working with complex, enterprise-level applications Nice-to-haves: Experience testing Power BI or Superset dashboards Familiarity with Python/C# backend services Background in data pipelines, AI/ML testing, or analytics platforms Azure or cloud data processing environment exposure Great plus: Automated testing tools What We Offer • Fully remote, long-term B2B opportunity • Modern tech stack with the latest tools and frameworks • Collaborative culture with knowledge sharing

🏢 Summary: Design and scale high-throughput data pipelines on cloud platforms to support advanced analytics and AI-driven products. The role focuses on building distributed data architectures in AWS and Databricks, ensuring performance, governance, and data quality. You will collaborate with AI/ML teams to deliver scalable, production-grade data solutions. 🗂️ Requirements: 3+ years of data engineering experience, Strong Python programming skills, Experience with Spark or Scala, Experience building distributed data pipelines in cloud environments, Knowledge of data modeling and data warehousing principles, Bachelor’s or Master’s degree in Computer Science or Engineering 📃 Skills: Python, Spark, Scala, AWS, Glue, EMR, Fargate, StepFunctions, Databricks, SQL, APIs, GenAI, GraphDB 🏢 Description: Data Engineer – Cloud & AI Platforms We’re looking for a Data Engineer to design and scale high-throughput data pipelines supporting advanced analytics and AI-driven products. What You’ll Do Architect and maintain distributed data pipelines in Databricks and AWS (Glue, EMR, Fargate, Step Functions) Ingest and process large volumes of structured and unstructured data (internal, market, third-party, alternative sources) Collaborate with AI/ML and engineering teams to design scalable data architectures and APIs Optimize performance and cost using Spark and cloud-native best practices Implement data governance, privacy, lineage, and access controls Build automated validation, monitoring, and data quality frameworks Evaluate emerging GenAI and data tooling to enhance platform capabilities What You Bring 3+ years of experience in data engineering Strong Python and experience with Spark or Scala Proven experience building distributed pipelines in cloud environments Solid understanding of data modeling, architecture, and warehousing principles Innovative problem-solving mindset Bachelor’s or Master’s degree in Computer Science or Engineering Nice to have: Experience with graph databases.

🏢 Summary: Hands-on Lead Cloud Platform Engineer role focused on owning, securing, automating, and optimizing an Azure-based SaaS and AI infrastructure, including Azure Databricks and DevSecOps practices. The position covers end-to-end cloud governance, Kubernetes operations, CI/CD, security, and cost optimization in a large-scale data environment. This is a senior technical role with potential growth into technical or people leadership. 🗂️ Requirements: 7+ years in DevOps/Cloud/Platform/Infrastructure engineering, 4+ years hands-on Azure production experience, Strong Azure governance, RBAC, policies, networking, storage, monitoring, security, Experience with Entra ID and Microsoft 365 administration, Production Kubernetes experience (AKS preferred), Experience managing Azure Databricks infrastructure, CI/CD experience with Azure DevOps and/or GitHub Actions, Scripting skills (PowerShell, Azure CLI, Bash, or Python), Knowledge of cloud security principles (least privilege, secrets management, encryption, auditability), Ability to work independently and drive platform improvements 📃 Skills: Azure, Databricks, DevSecOps, AKS, Kubernetes, EntraID, Microsoft365, AzureDevOps, GitHubActions, Terraform, Bicep, ARM, PowerShell, AzureCLI, Bash, Python, ADLS, Blob, AzureSQL, KeyVault, VNets, DNS, NSG, RBAC, CI/CD, FinOps, Defender, SOC2 🏢 Description: An AI-powered consumer intelligence platform is hiring a Lead Cloud Platform Engineer (Azure, Databricks & DevSecOps). Our platform processes 50+ billion data points monthly to deliver actionable insights for leading global brands, such as Coca-Cola, Unilever, and Bayer. We run on Azure, Databricks, and GenAI workflows, and we're scaling our platform for faster delivery, stronger security, and tighter cost discipline. The Role We're hiring a hands-on cloud platform leader to own, secure, automate, and optimize our Azure-based SaaS and AI infrastructure end to end. You'll own Azure administration, Entra ID and Microsoft 365, networking, storage, Kubernetes, security, CI/CD, and FinOps, plus the cloud platform layer around Azure Databricks (networking, identity, secure access, compute governance, monitoring, and cost control). This is a hands-on role first, with a clear path to technical or people leadership as the company grows. What You'll Do • Own Azure subscriptions, governance, networking (VNets, private endpoints, DNS, NSGs), storage (ADLS Gen2, Blob), Azure SQL, Key Vault, and monitoring across all environments. • Administer Entra ID and Microsoft 365 - identities, MFA, Conditional Access, least-privilege access, and joiner/mover/leaver processes. • Manage Azure Databricks infrastructure and security: secure connectivity, networking, identity integration, compute policies, audit logging, and cost controls. • Build and improve CI/CD (Azure DevOps, GitHub Actions) and introduce Infrastructure as Code (Terraform, Bicep, or ARM) to replace manual portal changes. • Operate and secure Kubernetes/AKS environments and supporting application infrastructure. • Strengthen security and DevSecOps posture; support SOC 2 evidence, security questionnaires, and enterprise reviews. • Build monitoring, dashboards, runbooks, and incident response across the platform. • Drive Azure and Databricks cost optimization through tagging, budgets, alerts, and compute tuning. What You'll Bring: Required Qualifications • 7+ years in DevOps, Cloud, Platform, Infrastructure, or Systems Engineering, including 4+ years hands-on with Azure in production. • Strong Azure depth: subscriptions, RBAC, policies, networking, storage, monitoring, and security. • Solid experience with Entra ID and Microsoft 365 administration. • Production Kubernetes experience (AKS preferred). • Experience managing or supporting Azure Databricks infrastructure (workspace admin, compute, networking, secure storage access, identity, audit logging). • CI/CD experience with Azure DevOps and/or GitHub Actions, plus scripting (PowerShell, Azure CLI, Bash, or Python). • Strong grasp of cloud security: least privilege, secrets management, encryption, and auditability. • Proven ability to work independently, create structure, and drive improvements with minimal supervision. Nice to Have: Preferred Qualifications Experience in AI, data, analytics, or SaaS environments; Infrastructure as Code (Terraform, Bicep); SOC 2 / ISO 27001 / GDPR; Microsoft Defender for Cloud and DevSecOps tooling; FinOps and Databricks cost optimization; and working with distributed or offshore teams. What This Role Is Not This role is not a pure CI/CD role, a helpdesk-only M365 admin role, a Databricks notebook developer, a data engineering role, or a people-management role. The Opportunity • Long-term B2B contract opportunity • High Impact: We’re backed by world-class investors and work with the most iconic global consumer brands. • You’ll have ownership, with a clear path to technical or people leadership as the company grows..

🏢 Summary: Principal Data Platform Engineer role focused on architecting and implementing a centralized Databricks lakehouse platform, including governance, ETL frameworks, and large-scale data migration. The position centers on building scalable, cost-efficient, and well-governed data infrastructure across multiple product lines. It involves leading multi-terabyte legacy migrations and establishing robust platform operations for analytics and AI workloads. 🗂️ Requirements: 5-8 years in data engineering or data platform roles, 3+ years hands-on experience with Databricks, Production experience with Unity Catalog and multi-catalog governance, Expert experience with Delta Lake optimization at multi-TB scale, Strong hands-on experience with Delta Live Tables, Experience with Databricks Workflows for orchestration and monitoring, Strong proficiency in PySpark and Databricks SQL, Experience designing unified schemas across disparate data sources, AWS experience including S3, IAM, VPC, Infrastructure-as-code experience (Terraform preferred), Experience leading at least one significant platform build or migration project, Ability to architect data platforms and document technical decisions 📃 Skills: Databricks, UnityCatalog, DeltaLake, DeltaLiveTables, PySpark, DatabricksSQL, AWS, S3, IAM, VPC, Terraform, SQL, ETL, Z-ordering, Compaction, Clustering 🏢 Description: Exactera has offices in New York City, Tarrytown NY, San Diego, CA, London, and Argentina. The Role As Principal Data Platform Engineer, you'll architect and implement our centralized data platform on Databricks. You'll establish governance patterns using Unity Catalog, optimize for cost and performance at scale, and enable our existing Data Engineers to build confidently on the platform. This is a data infrastructure role—focused on pipelines, storage, governance, and platform operations. The Business Challenge We operate multiple product lines (Transfer Pricing, R&D Services, RoyaltyStat, Provisioning), each with distinct databases containing enterprise financial data—journal entries, general ledgers, and financial statements. Our immediate challenge is migrating multi-terabyte datasets from legacy systems to a unified Databricks lakehouse while establishing governance patterns that enable multi-product operations at scale. What You'll Build Data Structuring: Design data models and implement unified schemas across multiple disparate product lines. Unity Catalog Architecture: Design and implement multi-catalog governance strategy supporting data isolation, cross-product data sharing, and comprehensive lineage tracking across our product portfolio Delta Lake Optimization: Establish patterns for Z-ordering, compaction, and liquid clustering at multi-TB scale. Define table structures, partitioning strategies, and retention policies that balance query performance with storage costs ETL Pipeline Framework: Build declarative pipeline patterns using Delta Live Tables. Create orchestration workflows for ingesting data from internal sources such as SQL databases and S3 Third Party Integrations: Integrate with third party data sources such as ERP systems (Netsuite etc.) and external data providers (S&P etc.) with automated ingest, robust error handling and monitoring. Platform Operations: Implement cost monitoring and optimization strategies, establish data quality frameworks, create self-service patterns enabling Data Engineers to work independently while maintaining governance standards Business Problems You'll Solve Key Legacy Product Migrations: Lead the architecture for migrating multi-terabyte datasets from legacy systems to Databricks—establishing patterns that will be reused across multiple product lines Multi-Product Data Architecture: Design Unity Catalog structures enabling secure data separation between product lines while allowing controlled cross-product analytics where appropriate Cost-Efficient Scale: Build infrastructure that scales efficiently—through intelligent caching, query optimization, and compute management strategies that avoid linear cost growth Platform Reliability: Establish monitoring, alerting, and data quality validation ensuring the platform operates reliably as foundation for both analytics and AI workloads Required Experience Databricks Expertise (Required) Unity Catalog: Production experience with multi-catalog governance, metastore design, and lineage tracking. Data Structuring: Experience designing and building unified schemas across multiple disparate product lines. Delta Lake: Expert-level experience with Z-ordering, compaction, liquid clustering, and performance tuning at multi-TB scale Delta Live Tables: Strong hands-on experience building declarative ETL pipelines, including change data capture and expectations/constraints Databricks Workflows: Experience with job orchestration, scheduling, and operational monitoring Business Intelligence: Experience enabling company-wide analytics and reporting with modern business intelligence tools and maintaining source of truth data and metrics. PySpark & Databricks SQL: Strong proficiency for code review, performance tuning, and query optimization Core Platform Engineering 5-8 years in data engineering or data platform roles, with 3+ years hands-on Databricks experience Track record leading at least one significant platform build or migration project AWS experience (S3, IAM, VPC) with ability to collaborate on infrastructure decisions Infrastructure-as-code experience (Terraform preferred) Technical Leadership Demonstrated ability architecting data platforms from first principles and defending technical decisions Strong written and verbal communication— document architecture decisions and present to both technical and business stakeholders Preferred But Not Required Experience with financial data, accounting systems (NetSuite), or enterprise ERP platforms Background building platforms that serve AI/ML workloads (experience preparing data for downstream ML consumption, RAG and retrieval, and LLMs. Understand advanced intelligence concepts such as relationship surfacing with knowledge graphs Familiarity with data governance frameworks and compliance requirements for regulated industries What We Offer:(The following only applies to US-based positions) A collaborative team culture with opportunities for career development. Ample opportunities to be recognized, build valuable skills, and grow your career. Generous vacation policy, including paid parental leave. Comprehensive health plans with FSA and HSA options. 401(k) retirement plan. Life and disability insurance coverage. Supplemental benefits like a dependent care savings plan, pet insurance, will preparation, and an employee assistance program. About Us: At Exactera, a FinTech SaaS start-up founded in 2016, we stand at the intersection of human and machine intelligence. Our corporate tax solutions are powered by AI and cloud-based technologies, serving customers worldwide. With over $100 million in funding from Savant Venture Fund and Insight Partners, we are poised for growth. We are committed to diversity, inclusion, and equal opportunities for all.

🏢 Summary: Senior Data Scientist role focused on building and operating real-time, AI-driven decisioning systems that directly impact user experience and conversion rates. The position involves developing and deploying production-grade machine learning models with strict low-latency requirements and contributing to personalization, experimentation, and recommendation systems. It combines hands-on ML engineering with experimentation, causal analysis, and large-scale data processing in a business-facing environment. 🗂️ Requirements: 7+ years commercial experience in Data Science with production ML focus, Strong experience deploying and maintaining production ML models, Experience with real-time systems and low-latency constraints, Proficiency in Python and SQL, Hands-on experience with gradient boosting methods, Experience with recommendation systems and uplift modeling, Strong knowledge of A/B testing and causal inference, Experience building feature engineering pipelines at scale, Experience with Apache Spark or PySpark, Cloud platform experience (AWS preferred), Familiarity with LLMs and GenAI for personalization 📃 Skills: Python, SQL, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, PySpark, Spark, AWS, Databricks, NLP, LLM, GenAI 🏢 Description: We are looking for a Senior Data Scientist to join a Growth Alliance team focused on AI-driven decisioning and user-facing machine learning systems. This role is suited for a technically strong data scientist who is equally comfortable working with business stakeholders and operating production ML systems at scale. About the Project: This is a high-impact role working directly on systems that influence end-user experience and conversion. You will operate in a real-time environment with strict low-latency requirements (<40 ms), contributing to a shift toward AI-driven decisioning aimed at improving product performance and increasing conversion rates. The work spans production ML pipelines, experimentation, and personalization, not just research or experimentation in isolation. Responsibilities: Design, develop, and deploy production machine learning models with real-time constraints; Build and maintain recommendation systems, uplift models, and contextual bandit frameworks; Conduct A/B testing, causal inference analyses, and statistical hypothesis testing; Develop and maintain feature engineering pipelines at scale using PySpark / Apache Spark; Monitor and validate deployed models to ensure performance and reliability; Collaborate closely with business stakeholders to challenge assumptions and define the right problems; Contribute to generative AI and LLM-based personalization initiatives; Work with decisioning platforms, personalization engines, and Martech integrations. Qualifications: 7+ years of commercial experience in Data Science with a strong production ML focus; Proficiency in Python (NumPy, Pandas, Scikit-learn) and SQL; Hands-on experience with gradient boosting methods (XGBoost, LightGBM); Experience with recommendation systems, clustering, NLP, and uplift modeling; Solid background in A/B testing frameworks, causal inference, and statistical modeling; Experience with feature engineering and production ML pipelines at scale; Cloud platform experience, preferably AWS; Familiarity with LLMs, prompt engineering, and GenAI for content personalization; Strong communication skills and ability to work in a highly business-facing environment; Databricks experience is a nice to have. Recruitment Process: Initial interview with our recruitment team; Interview with the hiring manager; Live Coding Assessment; Meeting with the Project Manager. Inuits Sp. z o.o. is registered in the National Register of Employment Agencies (KRAZ) under number 35420.

🏢 Summary: Senior Data Architect – Data Engineering role focused on designing and building scalable, cloud-native data infrastructure to power analytics, machine learning, and AI-driven logistics applications. The position combines enterprise data architecture with hands-on development of RAG systems, agentic workflows, and secure LLM integrations. This is a high-impact role with ownership over data platforms, AI systems, and distributed architecture in a production environment. 🗂️ Requirements: Bachelor's degree in Computer Science or equivalent practical experience, 8+ years of software or data engineering experience in production environments, Strong expertise in data modeling, distributed systems, and scalable cloud architectures, Hands-on experience with ETL/ELT frameworks and streaming technologies, Advanced SQL skills, Proficiency in Python and RESTful API development, Experience integrating LLM APIs into production applications, Strong understanding of system reliability, observability, and cost management in cloud environments 📃 Skills: Python, SQL, Snowflake, Databricks, Kafka, DBT, Spark, HEVO, Parquet, DeltaLake, Iceberg, LangChain, LangGraph, LlamaIndex, n8n, OpenAI, Anthropic, FastAPI, RAG, LLM, Microservices 🏢 Description: Senior Data Architect – Data Engineering Reports To: VP of Engineering FLSA Status: Exempt Compensation: $140,000 – $160,000 annually (based on experience) Position Summary Cargomatic is seeking a Senior Data Architect – Data Engineering to design and build scalable, cloud-native data infrastructure that powers analytics, machine learning, and AI-driven applications. This role combines deep data architecture expertise with hands-on experience in modern data platforms and LLM-enabled application development. You will lead the design of enterprise-grade data models, architect RAG systems, implement agentic workflows, and integrate secure, production-ready LLM capabilities into our ecosystem. This is a high-impact role with significant ownership, visibility, and opportunity to shape the future of intelligent logistics technology. Key Responsibilities Data Architecture & Engineering - Design and build scalable, cloud-native data pipelines (batch and streaming) supporting analytics, ML, and AI-powered applications - Architect enterprise-grade data models across data lakes, warehouses, and real-time systems (Snowflake, Databricks, Kafka, DBT) - Define standards for data governance, reliability, performance, and cost optimization - Optimize storage formats and distributed data systems (Parquet, Delta Lake, Iceberg) AI & LLM-Enabled Systems - Develop Retrieval-Augmented Generation (RAG) systems integrating structured and unstructured enterprise data - Design and implement agentic workflows using frameworks such as LangChain, LangGraph, LlamaIndex, n8n, or similar - Integrate LLM APIs (OpenAI, Anthropic, or similar) into secure, production-ready applications - Implement guardrails, validation layers, monitoring, and evaluation frameworks to mitigate hallucination, prompt injection, and data security risks Backend & API Development - Build secure backend APIs (Python/FastAPI) to expose AI-powered capabilities - Ensure observability, monitoring, and cost controls across AI and data services - Contribute to microservices architecture and distributed system design Collaboration & Leadership - Partner cross-functionally with Product, Engineering, and Operations to translate business requirements into scalable technical solutions - Mentor junior engineers and contribute to architectural standards and best practices - Drive innovation in data engineering and AI-powered logistics systems Qualifications - Bachelor's degree in Computer Science or equivalent practical experience - 8+ years of software or data engineering experience in production environments - Strong expertise in data modeling, distributed systems, and scalable cloud architectures - Hands-on experience with ETL/ELT frameworks and streaming technologies (Kafka, Spark, HEVO, Snowflake, DBT, etc.) - Advanced SQL skills and deep understanding of modern storage formats - Proficiency in Python and RESTful API development - Experience integrating LLM APIs into production applications - Strong understanding of system reliability, observability, and cost management in cloud environments Desired Experience - Experience building RAG pipelines including embeddings, vector search, chunking strategies, and hybrid retrieval - Experience designing multi-agent or agentic AI workflows with orchestration frameworks - Knowledge of LLM evaluation, monitoring, and tracing tools (LangSmith or similar) - Experience with microservices architecture and distributed system design - Exposure to transportation, logistics, or supply chain domains - Active GitHub contributions or demonstrated passion for emerging AI and data technologies Benefits - Medical, Dental, and Vision insurance - 401(k) with company match - Flexible Spending Accounts (FSA) - Company-paid Life and Disability insurance - Flexible Paid Time Off (PTO) and company holidays - Paid Parental Leave - Employee Assistance Program (EAP) - Opportunity to build cutting-edge AI solutions in a high-growth logistics technology company - Collaborative, high-impact team environment Cargomatic is proud to be an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace where all employees feel valued and empowered to succeed.

🏢 Summary: The offer is for a Senior Scala Engineer to design and evolve a high-performance aggregate system that optimizes query workloads across modern cloud data platforms. The role focuses on architecting intelligent prediction and selection logic, improving system scalability, and ensuring reliability in large-scale environments. You will work on accelerating business intelligence through advanced lifecycle and performance engineering. 🗂️ Requirements: 6+ years of professional software engineering experience, BA/BS in Computer Science or related field, Strong expertise in Scala, Experience with Akka or Pekko, Experience with data warehouse concepts, Experience with Docker and Kubernetes, Ability to design and optimize high-scale distributed systems, Ability to analyze system performance and trade-offs, Availability to work in Eastern/Central European Time hours 📃 Skills: Scala, Akka, Pekko, Snowflake, BigQuery, Databricks, Docker, Kubernetes 🏢 Description: Our customer is the semantic layer for modern data and AI. They bridge the gap between complex cloud data platforms—like Snowflake, Databricks, and Google BigQuery—and the business users who need consistent, AI-ready analytics. We are looking for a Senior Scala Engineer to join the Aggregates Engineering Team to help redefine how the world handles business intelligence. Responsibilities: Architect the Core Lifecycle: Design, build, and evolve the aggregate system that defines, manages, and optimizes aggregates to accelerate query performance across data platforms. Optimize Intelligent Performance: Architect the prediction and selection logic that identifies high-value aggregates and analyze query workloads to guide placement strategies. Scale High-Impact Systems: Improve maintenance and scheduling processes to ensure reliability and low overhead in large-scale environments, ensuring the system scales smoothly. Requirements: 6+ years of professional software engineering experience and a BA/BS in Computer Science or a related field. Strong programming expertise in Scala and ideally have experience with Scala worker systems like Akka or Pekko. You can reason about complex system behavior, lifecycle state, and performance tradeoffs while maintaining a focus on production reliability. Experience with data warehouse concepts (Snowflake, BigQuery, etc.) and containerization technologies (Docker, Kubernetes). Excellent communication skills and are comfortable working across query planning and platform teams. You are based in or able to work Eastern/Central European Time hours.