Top Hybrid Data Engineering Jobs in Boston, MA

Join Chewy as a Data Engineer I to learn and build ingestion and transformation pipelines, write SQL/Python for data prep, apply dbt Cloud modeling, monitor data quality, assist in triage, and collaborate with analytics and data science under senior mentorship while gaining exposure to Snowflake, Kafka, AWS, Terraform, and data observability.

The role involves leading teams to develop AI solutions addressing sustainability challenges, managing client accounts, and upholding ethical standards in technology integration.

The Data Engineer II will design data models, build ETL pipelines, collaborate with teams, and ensure data flows are efficient and well-monitored. A strong understanding of SQL, Python, and cloud technology is essential.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

Seek a Lead Analytics Engineer to design and build scalable data solutions, oversee data engineering, and collaborate with cross-functional teams to meet data needs.

Build and own end-to-end sales data pipelines and curated marts in Snowflake/dbt; implement monitoring, tests, and governance; run reverse-ETL to operational systems; partner with Sales, Ops, Finance, and People to deliver production-ready analytics products.

Manager of Data Platform Engineering responsible for ensuring data reliability, leading a data engineering team, setting platform standards, and collaborating with internal stakeholders to enhance data capabilities.

Design, build, and maintain scalable ETL/ELT pipelines using Python, Spark, Databricks, Airflow and SSIS. Integrate and cleanse diverse healthcare datasets, implement Unity Catalog for metadata and governance, optimize Spark performance and JVM tuning, support Medallion architecture, and collaborate with cross-functional teams to automate CI/CD, observability, and data quality processes.

The Senior Data Engineer will design and build data pipelines for usage and revenue reporting, manage Snowflake as the enterprise data warehouse, and collaborate with Finance to ensure data accuracy and quality.

Lead and deliver data-driven commercial analytics projects for life sciences clients, design/implement data platform and ETL solutions, analyze data and present insights, manage client relationships and project teams, mentor junior staff, and contribute to business development and internal data warehouse initiatives.