Senior Data Engineer

Formlabs•Somerville, MA

10h•$125,000 - $160,000•Onsite

About The Position

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 Senior 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.

Requirements

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.

Nice To Haves

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.
Familiarity with orchestration tools (Airflow, Dagster, Cloud Composer, or similar).
Experience with data quality, observability, or lineage tooling.
Exposure to business systems data — ERP, CRM, or eCommerce (NetSuite, Salesforce, Shopify, or similar).
Experience with Kubernetes or container orchestration.
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.

Responsibilities

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.