Data Engineer

Somerville · USD 80,000-125,000 / year · 3+ years experience · Full-time · Onsite · Posted Oct 7, 2026

At a glance

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.
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) 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.

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.
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.
Deploy and operate services on Cloud Run, instrument them with meaningful monitoring and alerting, and respond when something breaks.
Build well-documented, well-tested data models in BigQuery that analysts and stakeholders can use confidently, with clear lineage and sensible definitions.
Apply partitioning, clustering, incremental processing, and query optimization so that scale stays affordable and dashboards stay fast.
Work with analysts, software engineers, and business stakeholders to turn ambiguous questions into durable datasets and pipelines, rather than one-off extracts.
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.

3+ years of professional experience building production data pipelines, with strong, hands-on Python
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.
Experience with BigQuery or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including the design decisions that keep large tables performant and affordable.
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.
Experience with high-volume telemetry, event, IoT, or log data, where the size of the dataset materially changes how you design the pipeline.
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.

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.

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