Top Remote Senior Level Data Engineering Jobs in Boston, MA

Design, build, and maintain scalable ETL pipelines and data models to support pricing and analytics. Perform complex data profiling and analysis, enforce data quality and governance, collaborate with data scientists and stakeholders, mentor junior engineers, and support product launches using Python, SQL, Snowflake, and cloud tooling.

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 RevOps data modeling, ETL/DBT development, and dashboarding to ensure high-quality revenue data. Build Snowflake data marts, author complex SQL, create Tableau visualizations, apply AI/ML forecasting, and partner with Sales, Marketing, Finance, and IT to resolve data quality and reporting issues.

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.

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.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Design, build, and optimize Snowflake-based data platform: develop ELT pipelines, data models, governance, security, performance tuning, cost optimization, CI/CD, and advanced Snowflake features. Collaborate with stakeholders, mentor engineers, and deliver scalable data solutions for analytics.

Build and own end-to-end compliance data pipelines and models (case, alert, entity, transaction, risk features). Ensure data quality, monitoring, lineage, and audit-ready documentation. Partner with Compliance and engineering to close upstream gaps, improve detection signal quality, and implement measurement frameworks and reporting for control effectiveness.

Design and build Toast’s enterprise analytics layer and AI-ready semantic layer in the data warehouse. Collaborate with domain teams and data engineering to implement hub-and-spoke architecture, define complex business logic in SQL, support scalability, partner with stakeholders, and provide technical mentorship.