Data Engineer

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 Big Query — and across both of our data engineering stacks:
Real Time Integrations (RTI), which moves event data as it happens, and our Big Query 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.

• 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 Big Query 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 Big Query 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.

• 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 Big Query or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including the design decisions that keep large tables performant and affordable.
• GCP experience:
  Working knowledge of Google Cloud, ideally including Cloud Run, Pub/Sub, Cloud Storage, and IAM — or a demonstrated ability to pick up a new cloud quickly.
• Comfort at scale:
  Experience with high-volume telemetry, event, IoT, or log data, where the size of the dataset materially changes how you design the pipeline.
• Modern tooling:
  Fluency with Git/Git Hub, CI/CD pipelines, and containerized development and deployment.
• Excellent problem-solving, analytical, and communication skills, including the ability to explain data caveats to non-technical partners.
• Ability to work in a collaborative, fast-paced environment, and the ability to learn and implement new technologies quickly.
• Bachelor's degree in Computer Science, Engineering, a…