Lead Data Engineer - Cloud-Native Pipelines & Platform

Flagship Pioneering, Inc.; Cambridge (MA); On-site; USD 128,000 - 176,000; Full time

Job summary

Flagship Pioneering in Cambridge, MA is seeking a Lead Data Engineer to drive modern data infrastructure and pipelines. You will collaborate with Infrastructure & Operations, Lab IT, PI Tech, and science teams to translate evolving needs into secure, scalable data solutions.

Reporting to the Associate Director of Data Architecture & Engineering, you will implement core data platforms, set engineering standards, and mentor engineers while optimizing data warehouses, lakehouses, and data lakes

Qualifications

• 6+ years of hands-on data engineering experience, preferably in life sciences or healthcare.
• Cloud-native data architectures experience, especially AWS.
• Proficiency in Python and SQL for data manipulation and automation.
• Experience with modern data tooling (Dagster, dbt, Spark, Iceberg).
• Strong containerization and CI/CD practices; Git proficiency.

Responsibilities

• Lead complex data engineering projects across interdependent systems and cloud-first platforms.
• Oversee data infrastructure, platforms, tools, and data products.
• Ensure reliable data storage solutions (lakehouses, marts, warehouses) and scalable pipelines.
• Establish engineering standards for testing, documentation, release, and monitoring.
• Mentor engineers and champion best practices across teams.

Skills

Python

SQL

Data architecture

Leadership

Generative AI

Tools

Dagster

dbt

Spark

Iceberg

Lake Formation

Athena

Glue

Git

CI/CD

CDK

Docker

ECS

Job description

Flagship Pioneering in Cambridge, MA is seeking a Lead Data Engineer to drive modern data infrastructure and pipelines. You will collaborate with Infrastructure & Operations, Lab IT, PI Tech, and science teams to translate evolving needs into secure, scalable data solutions.

Reporting to the Associate Director of Data Architecture & Engineering, you will implement core data platforms, set engineering standards, and mentor engineers while optimizing data warehouses, lakehouses, and data lakes