Data Engineer

About the role

Department: Systems Location: Somerville, MA

Join Formlabs if you want to bring ground-breaking professional 3D printers to the desktop of every designer, engineer, researcher, and artist in the world.

As a Data Engineer at Formlabs, you will build and operate the pipelines that turn raw printer telemetry, manufacturing data, and business system data into trusted, query-ready datasets. Our printers generate telemetry at enormous scale, and the models built on top of it drive decisions across product, manufacturing, service, and go-to-market.

You will work in Python and SQL on Google Cloud — primarily Cloud Run and BigQuery — and across both of our data engineering stacks: Real Time Integrations (RTI), which moves event data as it happens, and our BigQuery batch and transformation layer. This is a hands-on building role for an engineer who is comfortable owning a pipeline end to end, from ingestion through modeling to the dashboards and services that consume it.

This role is based in our Somerville, Massachusetts, USA office and is a full-time position.

The Job:

- Build pipelines that scale: Design, develop, and maintain ingestion and transformation pipelines in Python and SQL that handle telemetry at very large scale — datasets measured in the trillions of rows — without sacrificing reliability or cost efficiency.

- Work across both stacks: Contribute to our Real Time Integrations (RTI) event-driven stack and our BigQuery batch and modeling stack, choosing the right tool for each problem rather than forcing everything through one pattern.

- Own it in production: Deploy and operate services on Cloud Run, instrument them with meaningful monitoring and alerting, and respond when something breaks.

- Model data people trust: Build well-documented, well-tested data models in BigQuery that analysts and stakeholders can use confidently, with clear lineage and sensible definitions.

- Engineer for cost and performance: Apply partitioning, clustering, incremental processing, and query optimization so that scale stays affordable and dashboards stay fast.

- Partner broadly: Work with analysts, software engineers, and business stakeholders to turn ambiguous questions into durable datasets and pipelines, rather than one-off extracts.

- Raise the bar: Apply modern software practices — version control, code review, automated testing, CI/CD, and infrastructure-as-code — to data work, and help improve how the team builds.

You:

- Python-first data engineering: 3+ years of professional experience building production data pipelines, with strong, hands-on Python — this is the core of the role, not a nice-to-have.

- A strong SQL foundation: Able to write, debug, and optimize complex SQL from scratch, and comfortable reasoning about query plans, joins, window functions, and aggregation at scale.

- Cloud data warehousing: Experience with BigQuery or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including the design decisions that keep large tables performant and affordable.