Top Hybrid Data Engineering Jobs in Boston, MA

Lead Data Engineer responsible for designing data models, implementing data solutions, managing data pipelines, and collaborating with teams to drive technical initiatives.

Lead design, develop, test, deploy and support cloud-based data engineering solutions. Work across Agile teams on distributed, real-time and batch data platforms, mentor engineers, and optimize code and data pipelines using big data, streaming, and cloud data warehousing technologies.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

Lead design, development, and delivery of enterprise data engineering and software solutions for PwC Tax. Drive client engagements, business development, architecture and integration, mentor teams, and ensure data integrity and compliance while promoting advanced engineering practices.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Manage the data architecture and strategic planning for the Sales team, optimizing processes through data and AI for targeting accounts and pricing strategies.

Lead a team to operate and scale CarGurus' data platform across ingestion, orchestration, storage, and observability. Own roadmap, SLAs, cost, security, and quality controls; improve developer experience; collaborate with product and stakeholders to prioritize initiatives, resolve incidents, and drive platform reliability and scalability.

Lead enterprise healthcare data modeling: design and maintain conceptual/logical/physical models, integrate varied clinical sources, implement common data model transformations, monitor data quality, and collaborate with clinicians, data scientists, and engineers.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

Own end-to-end data pipelines and production-grade dbt models (Snowflake) for Sales and Product analytics. Build governed data marts, dimensional models, and reverse ETL workflows. Improve data quality, testing, monitoring, and documentation. Partner with GTM, Product, and BI stakeholders, mentor peers, and contribute to platform tooling (Airflow, Terraform, AWS) to enable reliable, scalable analytics.

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality, automated tests, monitoring, and troubleshooting. Collaborate with product owners, data scientists, and engineers to translate business requirements into reliable data solutions and document data flows and technical specifications.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.