Top Data Engineer Jobs in Boston, MA

The Data Engineer II will design data models, build ETL pipelines, collaborate with teams, and ensure data flows are efficient and well-monitored. A strong understanding of SQL, Python, and cloud technology is essential.

Design, build, and maintain scalable ETL/ELT pipelines using Python, Spark, Databricks, Airflow and SSIS. Integrate and cleanse diverse healthcare datasets, implement Unity Catalog for metadata and governance, optimize Spark performance and JVM tuning, support Medallion architecture, and collaborate with cross-functional teams to automate CI/CD, observability, and data quality processes.

This co-op position involves developing data products, design and testing of data pipelines, and delivering insights from complex datasets for business decisions.

This co-op position involves developing data products and solving complex data problems using cloud technologies, SQL, and programming languages while gaining mentorship and business exposure.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

Build and own end-to-end sales data pipelines and curated marts in Snowflake/dbt; implement monitoring, tests, and governance; run reverse-ETL to operational systems; partner with Sales, Ops, Finance, and People to deliver production-ready analytics products.

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

As a Data Engineer, you will develop and manage the data infrastructure that supports HTS Media's advertising business, build ETL/ELT pipelines, and enable data-driven insights for advertisers and internal analytics.

The Data Developer & Analyst/Report Writer develops data pipelines, analytics solutions, and reports to support decision-making across Northeastern University.

Design, build, and validate pipelines to ingest, standardize, and publish real-world healthcare datasets (claims, EMR, registry). Perform SQL, Python, and SAS transformations, OMOP conversions, QA, and vendor communications to deliver high-quality datasets for analytics and research.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

The Lead Data Consultant will design and deliver scalable data solutions, manage project teams, and optimize client data environments, ensuring effective project execution and communication.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

The Data Engineer will build and optimize data pipelines, manage data flow, and collaborate with Analytics and Business teams to support data-driven insights and systems.

The Data Engineer will design and maintain ETL pipelines and data storage solutions, collaborating with cross-functional teams to ensure efficient data processing and delivery.

Transform raw engineering data into high-fidelity datasets for AI: ingest, clean, label, and create CAD reference models and engineering artifacts, build annotation workflows and tooling, collaborate with product/research/engineering/customer teams, and ensure dataset quality and manufacturability considerations for model training and evaluation.

The Data Engineer will build and maintain data infrastructure, design data pipelines, and develop reusable frameworks, leveraging technologies like Azure Databricks and PySpark for data processing and transformation.

The Data Engineer will enhance the Enterprise Data Warehouse, modernizing ETL processes, optimizing data transformations, ensuring data quality, and providing production support while collaborating with cross-functional teams.

Design, build, and operate Spark-based ETL pipelines on AWS. Collaborate with stakeholders to deliver reliable data products, ensure data quality and governance, optimize performance, and maintain operational excellence of the data platform.

The Data Engineer will assist in testing and deploying applications, implementing data quality and warehouse pipelines, and collaborating with various teams.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

Design, build, monitor, and optimize large-scale ETL/ELT data pipelines and data architecture. Integrate data sources, support analysts and data scientists, automate processes, ensure reliable data delivery, and write maintainable code.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.