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# Principal Data Engineer

- **Company:** Flagship Pioneering, Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $179,000.0 - $236,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Computational Biology, Continuous Integration, Information Engineering, Data Infrastructure, DevOps, Identity and Access Management, Python (Programming Language), Meta-Data Management, Operational Databases, SQL Databases, Feature Store, Data Ingestion, Large Language Models, Snowflake, Apache Spark, Cloudformation, Data Lakes, Machine Learning Operations, Databricks, Data Generation
- **Published:** October 27, 2026
- **Apply:** https://startup.jobs/principal-data-engineer-flagship-pioneering-inc-10605102

## About the Role

* 10+ years building and operating production data infrastructure, with a track record of architecting and owning platforms end to end.
 * Deep AWS expertise: S3, IAM, EC2, Batch, Lambda, and Spark (EMR or equivalent). Experience as technical owner of production cloud environments, including multi-account architecture and cross-account access.
 * Strong Python and SQL, with a background in production-grade pipeline development.
 * Production experience with workflow orchestration (Nextflow, Airflow, Temporal, Step Functions, Redun, or similar) and the judgement to evaluate and select among them.
 * Infrastructure as code (CloudFormation/CDK), CI/CD, containers, and observability. DevOps is a core competency for this role.
 * Data modelling and warehouse or lakehouse design, including metadata management, lineage, and data quality practice.
 * Cost governance at scale, including budgets, tagging, rightsizing, and storage lifecycle across hundreds of terabytes.
 * Fluency with scientific data semantics (studies, samples, donors, cell lines, assays) and the ability to partner directly with scientists to translate their questions into data models., * Familiarity with genetics and genomics data.
 * Microscopy or high-content screening data, including large binary object stores, transfer optimization, and image streaming for model training.
 * Databricks, OpenMetadata, or a comparable catalog in production.
 * Snowflake, particularly governed access patterns over cloud object storage.
 * ML infrastructure: SageMaker or Databricks ML, MLflow or Weights & Biases, feature stores.
 * Serving data to LLM or agent clients (MCP servers, tool APIs).
 * Experience leading data platform architecture as a senior or principal individual contributor, ideally in biotech, pharma R&D, or a research institute.

## Description

We are seeking a Principal Data Engineer to lead the architecture and evolution of the data platform underpinning our human genetics, multi-omics, and high-content imaging programs.

This is a senior, hands-on leadership role with broad technical authority. As the architectural lead for data engineering, you will set the direction for our AWS environment and the pipelines, catalog, and access layers built on it, defining the technical roadmap and the standards others build against. The scope spans core cloud data engineering (orchestration, warehousing, infrastructure as code, cost governance) and the distinctive challenges of scientific data: connecting each microscopy image to its cell line, donor, and study of origin, and enabling scientists to answer genotype questions directly, without specialist intervention.

You will join a focused, high-impact team supporting a research organization whose data generation and analytical ambitions are expanding rapidly. Our platform is established and productive, and is now entering a deliberate modernization phase. You will lead that transition, with significant influence over the platform's next-generation architecture.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact., * Serve as technical owner and architect of our AWS environment, including account structure, IAM, networking, compute, and storage.

 * Lead and extend cost governance, including budgets, alerting, tagging enforcement, rightsizing, and storage lifecycle and tiering.
 * Define and own developer operations for the data platform, including infrastructure as code, CI/CD, container builds, and secrets and environment management.
 * Upgrade our data lake, managed orchestration, metadata and data ingestion workflows to scale with future data production and enhance the infrastructure that transfers data from the lab to the cloud.
 * Partner with ML and computational biology leaders to define the interface between platform and modelling.