Lead Data Engineer

Flagship Pioneering, Inc.•Cambridge, MA

1d•$128,000 - $176,000

About The Position

Flagship Pioneering is a biotechnology company that invents and builds platform companies that change the world. Scientific Cloud is Flagship Pioneering's portfolio-facing IT organization, responsible for the cloud engineering, data and informatics engineering, research systems, lab systems, and vendor management capabilities that power Flagship's emerging companies. We are seeking a Lead Data Engineer to lead complex initiatives aimed at modernizing and professionalizing our data infrastructure, platforms, and pipelines. You will lead the implementation of core platform systems and help to set the directions and standards of our data infrastructure and pipelines. You will work closely with other engineers and stakeholders across Infrastructure & Operations (I&O), Lab IT, Pioneering Intelligence (PI) Tech, and the internal scientific community to turn evolving scientific needs into secure, reliable and scalable data engineering solutions. Reporting into the Associate Director, Data Architecture & Engineering, this is a technical, senior individual-contributor role that will champion engineering best practices, build complex data pipelines, lead data platform implementations, and handle the troubleshooting and optimization of data warehouse and storage solutions.

Requirements

6+ years of hands-on experience in data engineering, preferably in life sciences, biotech, or healthcare environments.
Proven expertise in designing and operating cloud-native data architectures, particularly within AWS.
Advanced proficiency in Python and SQL for data manipulation, pipeline development, and system automation.
Extensive experience with modern frameworks and tools such as Dagster, dbt, Spark, Iceberg, Lake Formation, Athena, Glue, etc.
Strong experience with Git, CI/CD, CDK, containerized workloads, Docker, and ECS.
Experience supporting both structured and unstructured data (CSV, JSON, Parquet, imaging, etc.).
Demonstrated success in leading complex, cross-functional projects involving technical and scientific stakeholders.
Practical experience using generative AI tools (e.g., Claude Code, GPT-based tools, etc.) to boost engineering velocity and reduce boilerplate.
A track record of leading complex technical initiatives under ambiguity, making explicit trade-offs and remaining accountable for reliable operational outcomes.

Nice To Haves

Experience supporting bioinformatics, cheminformatics, or clinical data workflows.
Familiarity with scientific software and ELN’s, particularly Benchling and CDD.
Exposure to Agile or Scrum-based development methodologies.
Relevant certifications (e.g., AWS Solutions Architect Associate, Data Analytics Specialty).

Responsibilities

Serve as the technical lead and subject matter expert on complex data engineering projects involving interdependent systems, legacy environments, and emerging cloud-first platforms.
Lead the implementation of data infrastructure, platforms, tools, and other data products.
Lead the optimization and reliability of data storage solutions (lakehouses, marts, warehouses, etc.).
Implement complex backend and data integration pipelines to support data lakes, marts, and other platforms.
Establish and reinforce engineering standards for testing, documentation, release management, and monitoring across the team.
Lead implementations for platform observability, management, resilience, capacity, and performance.
Diagnose and resolve complex failures spanning infrastructure and data pipelines; lead incident reviews and ensure corrective actions improve the wider system.
Collaborate across I&O, Lab IT, PI Tech, data science, and bioinformatics teams to ensure architectural alignment, reuse of components, and shared understanding of system states and dependencies.
Mentor junior engineers and establish engineering standards that drive excellence, reproducibility, and innovation across the team.
Leverage generative AI tools and frameworks to accelerate pipeline development, data transformation, and metadata enrichment at scale.