About the RoleMux 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.
What You'll DoOwn 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
Data Quality and Reliability- Keep the data accurate, tested and simple: move logic upstream, enforce contracts, and add observability
- Pay down tech debt as you go and leave the codebase simpler than you found it
Work AI-Forward- Use coding agents (e.g. Claude Code, Cursor) as a normal part of building, testing and refactoring pipelines and dbt models
- Look for places where agents and automation can replace manual data work, and build them
- Set standards for how AI tools touch production data: access, review, and what has to be verified by a human
Cross-Functional Partnership- Work with Engineering on how data is produced, changed and retired at the source
- Partner with Finance, FinOps, Product and GTM to understand the questions they need answered and decide which models get built first
- Work in engineering-style cycle planning with clear goals, commitments and status updates
Who You AreRequired Qualifications- 5+ years of data engineering experience, with a strong software engineering foundation
- You have built and run a data stack end to end in production. That means ingestion, warehouse, transformation, orchestration, and keeping it running after launch, not just one layer of it
- 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
Bonus- 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
- Experience designing evals for LLM output quality
- Data science experience: forecasting, regression, cohort or churn modeling, experiment analysis
- Experience running other data platforms beyond the warehouse, such as vector databases, time-series databases, ACID-compliant transactional databases, streaming systems (Kafka, Kinesis), and caches like Redis
- Reverse ETL into Salesforce, NetSuite or HubSpot
- Finance data experience: revenue, billing, COGS, cloud cost allocation
- Experience in developer tools, API-first or infrastructure companies; video streaming or CDN background is a plus
- Experience at a smaller company where you owned a broad scope
What Makes You Successful- You're equally comfortable debugging a Snowflake warehouse bill and writing a dbt test
- You think correct data matters more than clever AI, and you know the agent work only pays off once the data is right
- You use AI to go faster and still verify the output
- You see tech debt as something to remove, and you leave every codebase simpler than you found it
- You take ownership of problems end to end, from root cause to fix to validation
- You balance perfectionism with pragmatism: ship something good, then iterate
- You build strong relationships across functions and can explain technical tradeoffs to non-technical people
If you don't have all of these requirements but think your experience could be a great fit, that's okay! Please apply and we can talk about what's most needed in the role
We have a supportive culture that cares about both excellent work and work-life balance. We have office spaces in Downtown San Francisco, New York City, and London
U.S. Benefits- Flexible vacation + 11 company holidays
- Weekly no-meeting days + quarterly focus weeks
- Competitive health, dental, and vision insurance, including fertility
- FSA and HSA available
- Short-term and long-term disability insurance
- Group life insurance
- Travel accident insurance
- 401(k)
- Paid parental leave
- Professional Development program
- Lunch, internet, & ancillary equipment reimbursements
UK Benefits- Competitive annual leave policy (25 days annual leave plus 8 public holidays)
- Weekly no-meeting days + quarterly focus weeks
- Private medical and dental insurance, and a vision stipend, including fertility
- Short-term and long-term disability insurance
- Group life assurance
- Travel accident insurance
- Matched Pension
- Paid parental leave
- Professional development program
- Team social events
- Lunch, cafe, & ancillary equipment reimbursements
