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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Stream International Inc. - **Location:** Boulder, CO, United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, BigQuery, Code Review, Continuous Integration, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Cursor (Graphical User Interface Elements), Dimensional Modeling, Github, Identity and Access Management, Python (Programming Language), PostgreSQL, Operational Databases, Salesforce.Com, SQL Databases, Systems Integration, Usage Analysis, Google Data Studio, Delivery Pipeline, Stripe, Operational Systems, Terraform - **Published:** September 8, 2026 - **Apply:** https://www.builtincolorado.com/job/senior-data-engineer/11039269?handler=ApplyRedirect ## About the Role 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. You have: * 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 Bonus points: * 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 ## Description Own and evolve Stream's end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment. The summary above was generated by AI Senior Data Engineer, Revenue Operations About Stream 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 role 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Navigating Growth, Scaling Challenges, and Office Expansions with David Singleton, CTO at Stripe](https://www.wearedevelopers.com/videos/100362-navigating-growth-scaling-challenges-and-office-expansions-with-david-singleton-cto-at-stripe) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)