Top Remote Senior Level Data Engineering Jobs in Boston, MA

This role focuses on post-sale technical execution, ensuring customer onboarding and deployment, driving customer consumption of products, writing production code, diagnosing issues, and expanding adoption across teams.

Lead the development and implementation of data architecture strategies, mentor a team to deliver solutions, and collaborate with stakeholders to enhance resources while ensuring project success and quality outcomes.

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

As a Lead Data Engineer, you'll design and maintain data systems and pipelines using Snowflake, ensuring data quality and governance for AI applications.

Develop and productionize safety and performance metrics for autonomous driving by analyzing large-scale vehicle, simulation, and external data. Build scalable data pipelines, interactive visualizations, and analytics infrastructure to support safety assessment, monitoring, investigations, and stakeholder decision-making. Apply engineering judgment, signal processing, and statistics to derive analysis-ready features and evidence for launch readiness.

Lead the data engineering team, develop and maintain data models and pipelines for risk domains, and manage data quality and governance.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.