Senior Data Platform Engineer - Proteomics & ML

Flagship Pioneering Co-Op Program; Cambridge (MA); On-site; USD 108,000 - 209,000; Full time

Job summary

Expedition Medicines is seeking a senior data infrastructure engineer to design and own the data platform that makes proteomics data discoverable and usable at scale. You will build production-grade systems, coordinate with ML, chemistry, biology, and data science teams, and shape the technical direction of data infrastructure.

The role requires deep cloud experience (AWS/Azure), Kubernetes, and data workflow tooling (Flyte, Dagster, Airflow).

Qualifications

• Bachelor’s degree in Computer Science or related field; advanced degree is a plus.
• 8+ years of hands-on data infrastructure experience in biotech or life sciences.
• Proven ability to design, build, and operate terabyte-scale data systems.
• Cloud infrastructure (AWS, Azure), Kubernetes, and infrastructure-as-code tools (CFN, CDK, Terraform, ARM).
• Strong Python and SQL; comfortable with R.

Responsibilities

• Architect and build the core data platform that integrates experimental data with computational tools, with high availability and security.
• Own the technical roadmap for data infrastructure and data quality; drive leverage across teams.
• Automate workflows across proteomics environments and integrate LIMS for seamless data capture.
• Develop robust data pipelines using Flyte and other schedulers; design layered data architectures.
• Design cloud infrastructure with infrastructure-as-code and CI/CD to enable continuous improvement.

Skills

Python

SQL

Cloud infra

Kubernetes

Data modeling

Data pipelines

Flyte

Dagster

Airflow

DevOps

Terraform

CI/CD

API integration

REST

Education

Bachelor’s degree in CS/Data Eng

Tools

Dotmatics

CORE LIMS

Flyte

Dagster

Airflow

Job description

Expedition Medicines is seeking a senior data infrastructure engineer to design and own the data platform that makes proteomics data discoverable and usable at scale. You will build production-grade systems, coordinate with ML, chemistry, biology, and data science teams, and shape the technical direction of data infrastructure.

The role requires deep cloud experience (AWS/Azure), Kubernetes, and data workflow tooling (Flyte, Dagster, Airflow).