Senior Data Engineer

The AI orchestration of your wildest imagination.

n8n is the open workflow orchestration platform built for the new era of AI. We give technical teams the freedom of code with the speed of no-code, so they can automate faster, smarter, and without limits. Backed by a fiercely inventive community and 500+ builder-approved integrations, we’re changing the way people bring systems together and scale ideas for impact.

Since our founding in 2019, we’ve grown into a diverse team of over 260 - working across Europe and the US, connected by a shared builder spirit and with our centre of gravity in Berlin. Along the way, we’ve:

• Cultivated a community of more than 650,000 active developers and builders
• Earned 190K+ GitHub stars, making us one of the world’s Top 40 most popular projects
• Backed by top investors, from Sequoia’s first German seed to our SAP's recent strategic investment - bringing us to a $5.2bn valuation

That’s the company we’ve built. Now we’d love to see what you can build. If you’re applying, try n8n out - whether you’re technical or not - and share a screenshot of your first workflow with us. The easiest place to start is here: app.n8n.cloud/register http://app.n8n.cloud/register.

We’re in a defining moment of an incredible journey. Come and build with us.

Your main goal will be to build and scale the reliable data infrastructure that powers analytics, AI-driven decision-making, and smarter growth across n8n.

You’ll own critical parts of our modern data stack and help turn growing data needs into trusted, scalable systems:

BUILD AND OWN OUR DATA PIPELINES

• Design, build, and own end-to-end data pipelines and transformation workflows using dbt on BigQuery.
• Develop scalable ELT models that support analytics, AI, marketing, and operational use cases.
• Take ownership of existing pipelines while improving their reliability, maintainability, and performance.

SCALE ORCHESTRATION AND RELIABILITY

• Orchestrate and schedule data workflows using Dagster, ensuring pipelines are reliable, observable, and easy to operate.
• Build robust testing, monitoring, alerting, and recovery processes across the data platform.
• Improve pipeline architecture as workloads, data volumes, and business-critical use cases grow.

ENABLE ANALYTICS AND AI USE CASES

• Partner with analysts and data scientists to deliver trusted, well-modeled, self-serve datasets in Hex.
• Integrate and operationalize AI and LLM-driven workflows within the data platform.
• Help internal teams move from ad-hoc data requests to scalable and reusable data products.

POWER MARKETING ATTRIBUTION AND ACTIVATION

• Build marketing attribution models that improve visibility into campaign performance and customer conversion.
• Develop reverse ETL workflows that send accurate conversion data back to platforms such as Google Ads and Meta.
• Integrate external data sources, advertising APIs, and data vendors into reliable production pipelines.

SET DATA PLATFORM STANDARDS

• Establish and enforce standards for data quality, testing, documentation, observability, and deployment.
• Optimize BigQuery workloads for query performance, scalability, and cost efficiency.
• Contribute to data architecture decisions, mentor future hires, and help shape the technical direction of the data engineering function.

REQUIREMENTS

MUST-HAVES

• Advanced SQL: You have strong SQL skills and hands-on experience building production-grade data pipelines.
• dbt and BigQuery: You have production experience designing, maintaining, and optimizing data workflows using dbt and BigQuery.
• Data orchestration: You have used Dagster or a comparable tool such as Airflow or Prefect to orchestrate reliable production workflows.
• Python: You are comfortable using Python to build pipelines, automate processes, and develop data engineering tools.
• Data modeling: You understand dimensional modeling, ELT patterns, and how to design datasets for reliability, usability, and scale.
• Git and CI/CD: You have experience applying version control, automated testing, and deployment practices to data workflows.
• AI mindset: You are comfortable working in an AI-driven environment and are interested in applying LLMs to real data problems.

NICE-TO-HAVES

• Analytics tooling: You have experience with Hex or a similar notebook or BI tool such as Looker, Mode, or Tableau.
• AI-powered data applications: You have integrated LLM APIs or built data products and workflows powered by AI.
• Advanced Dagster: You have hands-on experience with Dagster assets, sensors, partitions, and production deployment patterns.
• Real-time data: You have worked with streaming technologies such as Pub/Sub, Kafka, or Dataflow.
• Data governance: You have experience implementing governance, access controls, lineage, or warehouse cost-management practices.
• Marketing data: You have built attribution …