Senior Data Engineer

About Mux

Mux is video for developers and their agents. We handle the hard problems of video, like encoding, streaming, delivery, and monitoring, along with the hard problem of understanding it: turning raw footage into context, structure, and answers. Video is a huge part of people’s lives, and we want to help make it not just playable, but understandable.

We’re committed to building a healthy team that welcomes a diverse range of backgrounds and experiences. We want people who care about our mission, are ready to grow, believe in our values (from Be Human to Turn Customers Into Fans), and want to make the people around them better.

You’ll be joining a tight-knit team with experience at places like Google, YouTube, Twitch, Zencoder, Fastly, and more. Our founders previously started (and sold) Zencoder, an early leader in cloud video technology, and authored Video.js, the biggest HTML5 video player on the web. We organize Demuxed, the premiere conference for video engineers in the world.

We’re backed by top investors like Coatue, Accel, Andreessen Horowitz, and Y Combinator. You’ll get to work with amazing companies: hundreds of startups, plus Patreon, Substack, Synthesia, and HubSpot. Customers large and small love working with us and love our team.

We are building something big together. We’d love to hear from you!

About the Role

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.

What You'll Do

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

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 Are

Required 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 ann