About the role
At Madlan, business users don't file ticket requests for dashboards. They ask questions in natural language and get answers directly — through Claude, connected to Looker and our other data tools over MCP. Claude is also our primary tool for writing code here.
That only works if what sits underneath is right. This role owns that foundation: the event instrumentation in our products, the pipelines feeding the warehouse, the models that give the data meaning, and the tests that prove it's correct. You're not producing charts — you're making hundreds of self-served answers trustworthy.
It's an engineering role, but not one you can do from behind the data. You'll need to understand the business well enough to know what a metric should mean before you model it.
Responsibilities
Instrumentation — Own product event tracking end-to-end: define the tracking plan with Product, drive implementation with our frontend and backend teams, audit what's actually firing, and build validation that catches regressions at deploy time rather than three months later.
Pipelines and warehouse — Build and operate batch and streaming pipelines into the warehouse; own its transformation layer, performance, data quality monitoring, and alerting.
Semantic layer — Own metric definitions and the modeled layer our AI and BI tooling query against, so core business terms resolve to one thing company-wide. Design models that hold up under open-ended questions, not just the ones you anticipated. Own lineage, documentation, and governance.
Business partnership — Work directly with Product, Sales, Customer Success, and Finance to understand what they're trying to measure, and push back when a proposed metric won't survive contact with reality.
Our stack
Redshift, Looker, Airflow, Kafka, AWS, Kubernetes, Python, SQL, TypeScript — with Claude and MCP as the layer connecting people to all of it.
Requirements
Requirements
5+ years building and operating production data infrastructure — pipelines, warehouses, and models you owned end-to-end
Deep SQL and strong dimensional modeling; hands-on with a cloud warehouse (Redshift, Snowflake, BigQuery)
2+ years of Python in production
Airflow or an equivalent orchestrator
Demonstrated ownership of product event instrumentation — tracking plan design, implementation with product engineers, and data quality validation
Able to read application code (TypeScript/Node.js)
Background in engineering or computer science
Advantage: Kafka or equivalent streaming · dbt or similar · LookML or another semantic layer · CI/CD and testing applied to data · building data for LLM/agent consumption · MCP · geospatial data · marketplace or high-traffic consumer products
