# Data Engineer at Formlabs > United States · Unspecified · Data engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Aformlabs%3A8262437 (read 2026-11-05). ## Facts (observed) - Company: [Formlabs](https://alderhq.app/companies/formlabs) - Location text: Somerville, MA; Somerville, Massachusetts - Where it can be done from (inferred): United States (evidence: "Location: Somerville, MA; Somerville, Massachusetts") - Posted: 2026-10-07 (28 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://careers.formlabs.com/job/8262437/apply/?gh_jid=8262437 - Skills listed (inferred): Python, Kafka, GCP, Snowflake, dbt, SQL ## Pay and sponsorship - Posted pay: $80k–$125k per year (from the listing's pay field) - Market band for similar posted roles (Boston, n 8): p25 $112k, median $134k, p75 $161k - H-1B filings at this company for similar roles, FY26: 9, median offered wage $123k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) - Licensed UK sponsor: registered as "Formlabs Inc (UK) Limited" (checked 2026-11-03, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers)) ## Description (the employer's text) 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. - 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/GitHub, 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 quantitative field, or equivalent practical experience. ### Bonus Points - Experience with streaming and event-driven architectures (Pub/Sub, Kafka, Dataflow, or similar). - Experience with dbt or a comparable transformation and testing framework. - Experience instrumenting or consuming device, IoT, or hardware telemetry. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aformlabs%3A8262437 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.