Senior Data Engineer

As a Senior Data Engineer on the Data Team, you'll continue to build and improve the data platform the whole company relies on. You care deeply about data modeling and how data is consumed, not only how it's moved and stored. As a senior engineer, you'll set architectural patterns and standards, and you'll make decisions the team builds on as it scales.

What you'll do

• Build systems that turn complex usage and operational data into trustworthy datasets used for billing, financial reporting, and product decisions.
• Build and operate the data platform and pipelines that power these use cases.
• Integrate data from across the business — internal systems, third-party tools, and external partners — into one consistent platform.
• Own data modeling across the warehouse and set standards for how data is structured as we scale.
• Work mostly in Python and SQL, with Dagster as our orchestrator.
• Build and maintain the cloud infrastructure behind the platform, managed with Terraform.
• Work directly with stakeholders. Understand their problem, then deliver data they can trust.
• Design for correctness and observability. Build the tests, monitoring, and data quality checks that make our numbers reliable.

What you'll need

• You've designed and operated production data platforms.
• You have strong data modeling fundamentals.
• You put business value first and know how to make pragmatic trade-offs between speed and quality.
• You're comfortable working with ambiguity and can take an underspecified problem to a production solution.
• You use AI tools effectively as part of your engineering workflow.
• You're strong in Python and SQL, and experienced with modern cloud data platforms such as BigQuery, Snowflake, or Databricks.
• You've worked with data orchestration tools such as Dagster, Airflow, or Prefect.
• You're comfortable owning cloud infrastructure and working with infrastructure-as-code such as Terraform.

Nice to have

• Experience with GCP data services such as BigQuery, Pub/Sub, or Dataflow.
• Knowledge of stream processing.
• Experience with dbt or similar transformation and modeling frameworks.
• Comfort reading or contributing to application code in Go or Ruby for integrations.
• Experience with financial, billing, metering, or usage data where errors have real downstream consequences.