Top Hybrid Data Engineering Jobs in Boston, MA

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

The role involves building and improving WHOOP's data platform infrastructure, ensuring reliability and efficiency, and driving best practices across engineering and data workflows.

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

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.

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 Analytics Engineer will build and optimize data transformation models, assist with BI tools, support stakeholders, document processes, and implement AI tools, contributing to a data platform under mentorship.

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.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

Lead a team to design and operate scalable Databricks/lakehouse platforms, ETL/ELT pipelines, data models, BI dashboards, and productionized ML. Ensure data quality, governance, and deliver actionable analytics and KPIs to drive business decisions while mentoring engineers, scientists, and analysts.

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.