Mux Senior Data Engineer at Mux owns the data stack end to end, including ingestion, Snowflake warehouse, and dbt transformation layers. This role builds a foundation for AI-native analytics by ensuring data quality and semantic modeling.
Responsibilities
Mux makes video easy for developers. Our APIs handle encoding, streaming, playback and analytics for companies like Patreon, Substack and Robinhood.
We're changing how Mux uses its own data. We're not building more dashboards. We're building semantic models that are clean, tested and well documented, and putting AI agents on top of them so anyone at Mux can ask a question in Slack and get a correct answer. Agents on top of bad data just give confident wrong answers faster. So this role is fundamentally a data engineering role: the warehouse, the pipelines and the dbt models have to be right before any of the AI work is worth anything.
As a Senior Data Engineer on the Analytics team, you'll own the Mux data stack end to end, from ingestion through Snowflake and dbt to the semantic layer the agents query. The team is small, so you'll have a broad scope and your work will be visible. You'll also be expected to use AI tools heavily in your own work, and to help decide where they belong in the data stack and where they don't.
This role is a fit if you want to build the foundation an AI-native analytics function runs on, and you'd rather delete a model than add another one.
Own the Data Platform End to End
Own the Mux data warehouse (Snowflake), the ingestion layer and the transformation layer (dbt): architecture, administration, permissions, CI/CD, cost, ingestion standards and data quality
Build and maintain ingestion from production systems and business tools (Fivetran or equivalent) into the warehouse, and keep it in sync as the product changes
Run the platform day to day: orchestration, deployments, environment management, and incident response when something breaks
Push curated data back out to business systems like Salesforce, NetSuite and HubSpot
Semantic Models and Agents
Build the semantic models and data foundation behind the agents people at Mux use to answer business questions
Help measure whether those agents give correct answers
Qualification
5+ years of data engineering experienceDeep hands-on dbt experienceData contractsStrong Python for pipelinesReal day-to-day use of AI coding tools
Required
5+ years of data engineering experience, with a strong software engineering foundation
Deep hands-on dbt experience: model design, testing, macros, CI, and refactoring a large, messy project into something maintainable
Hands-on experience administering a cloud data warehouse (Snowflake preferred; BigQuery or Databricks considered), including permissions, performance tuning and cost management
Data contracts, data observability tooling (e.g. Metaplane, Monte Carlo, Elementary)
Expert SQL and strong data modeling skills (dimensional modeling, grain, slowly changing dimensions)
Strong Python for pipelines, automation and tooling
Experience with managed ingestion tools (e.g. Fivetran, Airbyte) and comfort working in AWS or GCP
Real day-to-day use of AI coding tools, plus a clear sense of where they help and where they get things wrong
A working foundation in analysis and statistics: you can take a business question, pull the data, sanity-check it, and present a clear answer without handing it off
You care about data quality and governance, and build things to last
Experience building semantic layers (dbt Semantic Layer / MetricFlow, Cube, Omni, LookML or similar)
Experience building or integrating LLM agents over structured data: text-to-SQL, MCP servers, retrieval over metric definitions
