Data Engineer

Matterworks•Somerville, MA

17d•Hybrid

About The Position

Matterworks is seeking a Data Engineer to build and run the pipelines behind our models. Our platform acquires mass spectrometry and molecular data at scale and turns it into the datasets our AI team trains on and our product reasons over. You will own real pieces of that path end to end. Our data serves two different customers. The AI team needs training corpora that are complete, correctly split, and reproducible. The product and agentic layer needs values a scientist can explain and we can safely show a customer. You will build inside the contracts and checks that keep both honest, and you will help extend them. This is a hands-on role with a clear growth path. You will start by owning well-scoped pipelines and datasets and grow toward owning larger parts of the platform. You will report to the Head of Engineering and work daily with our machine learning researchers, scientists, and product team.

Requirements

2+ years of professional experience building data pipelines in production.
Proficient in Python and SQL.
Working knowledge of cloud data infrastructure.
Demonstrated experience owning a pipeline or dataset end to end, including the tests, the monitoring, and the failures.
Comfort with messy data and messy formats, and the patience to track down why two sources disagree.
Daily use of AI coding tools, paired with healthy skepticism about their output on questions of production data correctness.
Clear written communication, particularly when explaining what broke and what you changed.
Curiosity about the science.

Nice To Haves

We run Argo Workflows and Metaflow on EKS, Glue and Athena over Apache Iceberg and Parquet, DuckDB, and Terraform. Depth in any comparable stack transfers fine.
Experience in life sciences, biotechnology, or biochemistry is a plus but not a requirement, and you will work alongside strong in-house chemistry every day.
A passion for contributing to an early-stage startup where autonomy, eagerness to learn, and enthusiasm for solving novel scientific challenges prevail over rigid processes and egos.

Responsibilities

Build and Operate Pipelines: Own well-scoped pipelines end to end: designed, tested, instrumented, documented, and running on a schedule.
Labels and Enrichment: Turn raw data into datasets people can actually use, with consistent schemas, trustworthy metadata, and documented definitions.
Data Quality Checks: Design, build and extend our quality checks so that each build gets compared against the last one before it publishes, and a failure stops the pipeline instead of shipping.
Ingest and Acquisition: Bring new public and partner datasets into the platform: fetching, converting, validating, and reconciling them against what we already hold. Expect messy scientific and vendor formats and file that require continuous improvements to our systems to handle at scale.