Top Data Engineer Jobs in Boston, MA

Design, develop, test, and deploy cloud-based data pipelines and warehousing solutions (Snowflake, AWS). Build ETL/API integrations (Guidewire), apply Data Vault methodology, ensure data quality, mentor junior engineers, and collaborate in agile squads to deliver analytics and reporting solutions.

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.

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.

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.

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

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.

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 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.

The Data Engineer will manage data extraction pipelines, modernize data systems, implement monitoring tools, optimize workflows, and support compliance requirements using various tools and platforms.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

The Staff Data Engineer will build and manage a data lake, optimize data pipelines, collaborate with teams, and support AI projects while ensuring quality data solutions.

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

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

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Design, build, and support scalable ETL/ELT data pipelines across IBM Netezza, DataStage and modern cloud platforms (Snowflake, Databricks). Develop complex SQL, troubleshoot production pipelines, and collaborate with BI and cloud teams for data ingestion, transformation, validation, and documentation.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Lead modernization of core data systems and build scalable, secure data pipelines for cloud and on‑prem environments. Design data contracts, observability, lineage, and real‑time processing for vulnerability management. Partner with product and leadership to align data strategy and deliver customer-facing data solutions.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.