Lead Data Engineer

Stream powers real-time Chat, Video, Activity Feeds, and AI Moderation for billions of end-users across thousands of apps, from Strava and Bumble to eBay and Patreon. Our platform processes billions of API requests per month and supports applications with millions of concurrent users, while delivering highly reliable, low-latency services and a great developer experience.

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.

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• 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

• Revenue Operations or GTM data experience

• Salesforce and Stripe data modeling

• Product analytics platforms such as PostHog

• Marketing attribution and funnel analytics

• MRR, expansion, contraction, churn, and revenue reconciliation logic

• Working closely with business stakeholders while keeping engineering discipline

• GCP depth, including IAM, service accounts, and BigQuery cost optimization

• You own the data platform the whole company runs 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 foundation that already works, built from scratch, so you evolve and harden it rather than start from zero.

• You want a broad, ambiguous platform to own and the autonomy to make calls on it

• You ship fast and learn fast, even when things are unfinished

• You're comfortable working with teammates across time zones

You probably won't if you want tightly scoped tickets, a fully defined process before acting, or a slow and highly predictable environment.