Lead Data Engineer, Stream

• You like owning a platform end to end and staying hands-on while you do it. You're comfortable in a small team, and you're energized by building rather than by growing an org around you. You influence through the work: architecture, code review, and clear conventions, not a title
• 5+ years building and operating production data platforms
• Expert SQL and strong Python
• Experience designing incremental, idempotent, well-tested pipelines
• Solid experience with BigQuery or another modern cloud data warehouse
• Experience with modern ELT tooling such as SQLMesh, dbt, dltHub, Fivetran, or Airbyte
• Experience with orchestration and CI/CD (GitHub Actions, Airflow, or equivalent)
• Infrastructure-as-code experience with Terraform or a close equivalent
• Strong data modeling skills: dimensional modeling, warehouse design, testing, and observability

Desirable

• The data platform in this role is what our go-to-market and product decisions run on
• You'll own the pipelines, integrations, and central repository that bring Stream's data together, plus the models that turn it into something the business can trust
• We're mid-migration to GCP, so there's real architecture to shape
• Two things make this different from most data jobs
• A Revenue Operations team owns the stakeholder relationships and the business questions, so your time goes into building durable systems instead of chasing requirements
• And analytics translation is increasingly handled by AI, which is exactly why the engineering underneath it has to be right
• Data modeling is the core of this role
• It's a small team and a fast, unfinished environment
• High drive, sometimes hectic
• If you like owning a platform end to end, that's the appeal
• Build and evolve the ingestion platform. Python/dltHub pipelines loading into BigQuery, integrating Salesforce, Stripe, Postgres, PostHog, cloud billing, and other GTM systems. Design incremental loading, write dispositions, and scheduling, and make onboarding a new source predictable and low-risk
• Build the transformation layer. SQLMesh models across our layered architecture, clean and well-tested dimensional models, and clear conventions for grain, naming, and audit. Keep the core business models accurate: revenue waterfall, GTM funnel, marketing attribution, and product usage
• Improve reliability. Expand data quality and observability, build freshness checks, reconciliation tests, and execution monitoring. Take point when data is stale, wrong, or late, and trace issues across pipelines, transformations, and upstream systems
• Own the platform infrastructure. BigQuery and supporting GCP, plus Terraform, IAM, service accounts, scheduled jobs, and deployment workflows, tuned for security, reliability, and cost
• Enable the business. Deliver trusted datasets to Looker Studio, Google Sheets, and our internal CRM, and run reverse ETL back into operational systems like Salesforce
• Raise the technical bar. Help shape engineering standards and architecture, review pipeline and model changes, and share context with the analysts and engineers who contribute to the platform