Senior Data Engineer

About the Role

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.

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.

Requirements

• Python-first data engineering: 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.
• Cloud data warehousing: Experience with BigQuery or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including 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 demonstrated ability to pick up a new cloud quickly.
• Comfort at scale: Experience with high-volume telemetry, event, IoT, or log data, where dataset scale materially changes pipeline design.
• Modern tooling: Fluency with Git/GitHub, CI/CD pipelines, and containerized development and deployment.
• Soft skills: Excellent problem-solving, analytical, and communication skills; ability to collaborate in a fast-paced environment.
• Education: 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, etc.).
• Experience with dbt or comparable transformation and testing frameworks.
• Experience instrumenting or consuming device, IoT, or hardware telemetry.
• Familiarity with orchestration tools (Airflow, Dagster, Cloud Composer, etc.).
• Experience with data quality, observability, or lineage tooling.
• Exposure to business systems data (NetSuite, Salesforce, Shopify, etc.).
• Experience with Kubernetes or container orchestration.
• Ability to effectively leverage AI coding tools, particularly hands-on experience with Claude Code.