Data Engineer Jobs in Cambridge, MA

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

Lead architecture and implementation of a next-generation data layer for agentic AI. Design multi-tenant schemas and knowledge graph ontologies, optimize Snowflake and Kinetica clusters for high availability, define ETL/ELT and streaming patterns, and serve as the primary technical client liaison to align data architecture with business objectives and performance SLAs.

Design, build, and operate the core data warehouse, ingestion, orchestration, and cataloging platform. Develop batch and streaming pipelines, ensure data quality, governance, observability, and provide ML data platform capabilities. Lead architecture, improve platform reliability and performance, and collaborate with product, engineering, data science, security, and compliance teams.

Design, build, and maintain enterprise ETL/ELT pipelines and data management workflows for market, reference, and financial data in the Markit EDM platform. Implement ingestion, validation, reconciliation, enrichment, monitoring, and distribution processes; support reporting and downstream integrations. Troubleshoot production data issues, collaborate with stakeholders in agile squads, and mentor junior engineers to promote best practices.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

Designs and implements cloud-based single and multi-tenant data solutions, PoCs, real-time pipelines and ETL/ELT processes using Snowflake, AWS, Python and Informatica. Builds and tunes databases and stored procedures, develops APIs with Spring Boot/Java/Python, applies CI/CD, leads architecture and QA, manages project lifecycle, advises on risk and technical strategy, and mentors junior engineers.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Lead development of AI and ML solutions for healthcare systems, overseeing teams, driving use case development, and ensuring operational excellence while managing global delivery operations.

As a Data Engineer at Veeva, you will design and build robust data processing systems, implement ETL workflows, manage data architecture, and ensure data governance while collaborating with cross-functional teams.

As a Senior Data Engineer, you'll design scalable data models, build automated ETL pipelines, and mentor junior engineers while collaborating across teams.

The role involves developing and maintaining data workflows for research teams, managing scientific applications and data governance while collaborating with scientists and vendors to deliver scalable informatics solutions.

Lead and manage the Data Engineering team to support pharmaceutical research by developing and maintaining data solutions, collaborating with team members, and innovating processes.

The Engineer will operationalize scientific tools and methods within the Data Foundry, building deployment pipelines, model serving infrastructure, and ensuring APIs are robust and analytics-ready for both scientists and AI agents.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

The Automation Engineer designs, develops, and maintains data pipelines and API integrations for scientific workflows while ensuring compliance with industry standards. The role involves collaboration with scientists and the use of various technologies to optimize data processes.

Design and execute RIS data migration strategies: gather requirements, extract and map legacy clinical/administrative data, perform ETL transformations, load master lists, validate and verify migrations, document processes, train staff, and provide post-go-live support.

The Sr. Data Engineer will design and maintain data pipelines, enhance data reliability, and collaborate with trading strategists to improve platform capabilities.

The Senior Data Engineer will design, implement, and maintain data pipelines, improve data quality, and support investment data management, collaborating within a team to enhance decision-making and reporting capabilities.

The role involves building and maintaining scalable data infrastructure, designing data pipelines, and collaborating with various teams to optimize data processing. Responsibilities include ensuring system reliability and mentoring other engineers.

As a Software Engineer in the Data Platform team, you will implement data governance frameworks, manage access controls, improve data quality, and support audit readiness.

The Senior Software Engineer will design and implement software solutions for investment processes, engage with leaders, automate development, and support production escalations.

As a Senior Software Engineer, you will design APIs, manage database architecture, optimize performance, and collaborate with cross-functional teams to support the Data Platform Team's backend solutions.

As a Senior Data Engineer, you'll manage, optimize, and expand data infrastructure for a clinical data registry, focusing on enhancing data systems and reliability while developing new capabilities.