<p><strong>Department:</strong> Systems <strong>Location:</strong> Somerville, MA</p> <p>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.</p> <p>As a <strong>Data Engineer</strong> 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.</p> <p>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.</p> <p>This role is based in our Somerville, Massachusetts, USA office and is a full-time position.</p> <p><strong>The Job:</strong></p> <ul> <li><strong>Build pipelines that scale:</strong> 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.</li> <li><strong>Work across both stacks:</strong> 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.</li> <li><strong>Own it in production:</strong> Deploy and operate services on Cloud Run, instrument them with meaningful monitoring and alerting, and respond when something breaks.</li> <li><strong>Model data people trust:</strong> Build well-documented, well-tested data models in BigQuery that analysts and stakeholders can use confidently, with clear lineage and sensible definitions.</li> <li><strong>Engineer for cost and performance:</strong> Apply partitioning, clustering, incremental processing, and query optimization so that scale stays affordable and dashboards stay fast.</li> <li><strong>Partner broadly:</strong> Work with analysts, software engineers, and business stakeholders to turn ambiguous questions into durable datasets and pipelines, rather than one-off extracts.</li> <li><strong>Raise the bar:</strong> 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.</li> </ul> <p><strong>You:</strong></p> <ul> <li><strong>Python-first data engineering:</strong> 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.</li> <li><strong>A strong SQL foundation:</strong> Able to write, debug, and optimize complex SQL from scratch, and comfortable reasoning about query plans, joins, window functions, and aggregation at scale.</li> <li><strong>Cloud data warehousing:</strong> Experience with BigQuery or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including the design decisions that keep large tables performant and affordable.</li> <li><strong>GCP experience:</strong> 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.</li> <li><strong>Comfort at scale:</strong> Experience with high-volume telemetry, event, IoT, or log data, where the size of the dataset materially changes how you design the pipeline.</li> <li><strong>Modern tooling:</strong> Fluency with Git/GitHub, CI/CD pipelines, and containerized development and deployment.</li> <li>Excellent problem-solving, analytical, and communication skills, including the ability to explain data caveats to non-technical partners.</li> <li>Ability to work in a collaborative, fast-paced environment, and the ability to learn and implement new technologies quickly.</li> <li>Bachelor's degree in Computer Science, Engineering, a quantitative field, or equivalent practical experience.</li> </ul> <p><strong>Bonus Points:</strong></p> <ul> <li>Experience with streaming and event-driven architectures (Pub/Sub, Kafka, Dataflow, or similar).</li> <li>Experience with dbt or a comparable transformation and testing framework.</li> <li>Experience instrumenting or consuming device, IoT, or hardware telemetry.</li> <li>Familiarity with orchestration tools (Airflow, Dagster, Cloud Composer, or similar).</li> <li>Experience with data quality, observability, or lineage tooling.</li> <li>Exposure to business systems data — ERP, CRM, or eCommerce (NetSuite, Salesforce, Shopify, or similar).</li> <li>Experience with Kubernetes or container orchestration.</li> <li>Ability to effectively leverage AI coding tools — particularly hands-on experience with Claude Code, including building reusable Skills, subagents, or plugins that encode team workflows and make them repeatable.</li> </ul> <p><strong>Our Perks & Benefits:</strong></p> <ul> <li>Robust equity program to build