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 a fast, unfinished environment, and you're energized by building rather than by growing a large org around you
• You lead through the work: architecture, code review, and mentoring, not a management title
• 6+ years building and operating production data platforms
• Expert SQL and strong Python
• Experience designing incremental, idempotent, well-tested pipelines
• Deep 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
• Strong data modeling skills: dimensional modeling, warehouse design, testing, and observability
• A track record of technical leadership through architecture, code reviews, and mentoring

Desirable

• We're looking for a Lead Data Engineer to join Stream on our mission of elevating the quality of apps for billions of users globally
• You'll be the technical owner of the data platform our go-to-market and product decisions run on
• You'll own the pipelines, integrations, and central repository that bring Stream's data together, and the models that turn it into something the business can trust
• A Revenue Operations team owns the stakeholder relationships and business questions, so your time goes into building durable systems rather than chasing requirements
• Analytics translation is increasingly handled by AI, which is exactly why the engineering underneath it matters. Strong data modeling is at the core of this role
• We're mid-migration to GCP, so there's real architecture to shape and own
• 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, and lead incident response when data is stale, wrong, or late. 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
• Set technical direction: define engineering standards and architecture, review pipeline and model changes, and mentor the engineers and analysts who contribute to the platform
• What makes this role exciting:
• You own the data platform the whole company depends on, not one pipeline or one domain
• Your work powers forecasting, commissions, pricing, churn analysis, product insight, and board reporting. The quality of your engineering shows up directly in how the company runs
• The stack is modern and AI-forward: Python, dlt, SQLMesh, BigQuery, Terraform, GitHub Actions, with Claude, Cursor, and Linear across the team
• You inherit a solid foundation built from scratch, so you get to evolve and harden it rather than start from zero