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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** Stream - **Location:** Amsterdam, Netherlands - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, BigQuery, Code Review, Continuous Integration, 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, Usage Analysis, Google Data Studio, Delivery Pipeline, Stripe, Operational Systems, Terraform - **Published:** July 12, 2026 - **Apply:** https://nl.indeed.com/viewjob?jk=f5d1e5baa43044b5 ## About the Role * 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 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 * Deep GCP familiarity, including IAM, service accounts, and BigQuery cost optimization ## Description 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., * 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., * 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. ## 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) - [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) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [The Best X (Twitter) Accounts for Developers](https://www.wearedevelopers.com/magazine/294-the-best-x-twitter-accounts-for-developers)