# Senior Data Engineer [Mux](https://gurify.com/jobs?q=Mux) · Remote, United States · Posted yesterday Remote Senior [Data](https://gurify.com/jobs/data) [Apply on the original posting → (opens in a new tab)](https://jobs.ashbyhq.com/mux/bf4007f4-3610-4a67-acd6-0e7a7f11dbbe) ## Job description ### 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 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 Mux is an Equal Opportunity employer committed to building a diverse company. We believe diversity makes us better, and we strive to be inclusive and equitable. That’s why we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. **Live in Mux’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. 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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\u2019s lives, and we want to help make it not just playable, but understandable.\u003C/p\u003E\u003Cp\u003EWe\u2019re 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.\u003C/p\u003E\u003Cp\u003EYou\u2019ll 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.\u003C/p\u003E\u003Cp\u003EWe\u2019re backed by top investors like Coatue, Accel, Andreessen Horowitz, and Y Combinator. You\u2019ll 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.\u003C/p\u003E\u003Cp\u003EWe are building something big together. We\u2019d love to hear from you!\u003C/p\u003E\u003Ch3\u003EABOUT THE ROLE\u003C/h3\u003E\u003Cp\u003EMux makes video easy for developers. Our APIs handle encoding, streaming, playback and analytics for companies like Patreon, Substack and Robinhood.\u003C/p\u003E\u003Cp\u003EWe\u0026#39;re changing how Mux uses its own data. We\u0026#39;re not building more dashboards. We\u0026#39;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.\u003C/p\u003E\u003Cp\u003EAs a Senior Data Engineer on the Analytics team, you\u0026#39;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\u0026#39;ll have a broad scope and your work will be visible. You\u0026#39;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\u0026#39;t.\u003C/p\u003E\u003Cp\u003EThis role is a fit if you want to build the foundation an AI-native analytics function runs on, and you\u0026#39;d rather delete a model than add another one.\u003C/p\u003E\u003Ch3\u003EWHAT YOU\u0026#39;LL DO\u003C/h3\u003E\u003Ch3\u003EOWN THE DATA PLATFORM END TO END\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EOwn the Mux data warehouse (Snowflake), the ingestion layer and the transformation layer (dbt): architecture, administration, permissions, CI/CD, cost, ingestion standards and data quality\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBuild and maintain ingestion from production systems and business tools (Fivetran or equivalent) into the warehouse, and keep it in sync as the product changes\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ERun the platform day to day: orchestration, deployments, environment management, and incident response when something breaks\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPush curated data back out to business systems like Salesforce, NetSuite and HubSpot\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ESEMANTIC MODELS AND AGENTS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EBuild the semantic models and data foundation behind the agents people at Mux use to answer business questions\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHelp measure whether those agents give correct answers\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EDATA QUALITY AND RELIABILITY\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EKeep the data accurate, tested and simple: move logic upstream, enforce contracts, and add observability\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPay down tech debt as you go and leave the codebase simpler than you found it\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWORK AI-FORWARD\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EUse coding agents (e.g. Claude Code, Cursor) as a normal part of building, testing and refactoring pipelines and dbt models\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ELook for places where agents and automation can replace manual data work, and build them\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESet standards for how AI tools touch production data: access, review, and what has to be verified by a human\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ECROSS-FUNCTIONAL PARTNERSHIP\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EWork with Engineering on how data is produced, changed and retired at the source\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPartner with Finance, FinOps, Product and GTM to understand the questions they need answered and decide which models get built first\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWork in engineering-style cycle planning with clear goals, commitments and status updates\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWHO YOU ARE\u003C/h3\u003E\u003Ch3\u003EREQUIRED QUALIFICATIONS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003E5\u002B years of data engineering experience, with a strong software engineering foundation\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou 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\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDeep hands-on dbt experience: model design, testing, macros, CI, and refactoring a large, messy project into something maintainable\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHands-on experience administering a cloud data warehouse (Snowflake preferred; BigQuery or Databricks considered), including permissions, performance tuning and cost management\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EData contracts, data observability tooling (e.g. Metaplane, Monte Carlo, Elementary)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExpert SQL and strong data modeling skills (dimensional modeling, grain, slowly changing dimensions)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong Python for pipelines, automation and tooling\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with managed ingestion tools (e.g. Fivetran, Airbyte) and comfort working in AWS or GCP\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EReal day-to-day use of AI coding tools, plus a clear sense of where they help and where they get things wrong\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EA 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\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou care about data quality and governance, and build things to last\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EBONUS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EExperience building semantic layers (dbt Semantic Layer / MetricFlow, Cube, Omni, LookML or similar)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience building or integrating LLM agents over structured data: text-to-SQL, MCP servers, retrieval over metric definitions\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience designing evals for LLM output quality\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EData science experience: forecasting, regression, cohort or churn modeling, experiment analysis\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience 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\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EReverse ETL into Salesforce, NetSuite or HubSpot\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFinance data experience: revenue, billing, COGS, cloud cost allocation\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience in developer tools, API-first or infrastructure companies; video streaming or CDN background is a plus\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience at a smaller company where you owned a broad scope\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWHAT MAKES YOU SUCCESSFUL\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EYou\u0026#39;re equally comfortable debugging a Snowflake warehouse bill and writing a dbt test\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou think correct data matters more than clever AI, and you know the agent work only pays off once the data is right\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou use AI to go faster and still verify the output\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou see tech debt as something to remove, and you leave every codebase simpler than you found it\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou take ownership of problems end to end, from root cause to fix to validation\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou balance perfectionism with pragmatism: ship something good, then iterate\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E- You build strong relationships across functions and can explain technical tradeoffs to non-technical people\u003Cbr /\u003E \u003Cbr /\u003E If you don\u0026#39;t have all of these requirements but think your experience could be a great fit, that\u0026#39;s okay! Please apply and we can talk about what\u0026#39;s most needed in the role\u003C/p\u003E\u003Cp\u003EWe 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\u003C/p\u003E\u003Ch3\u003EU.S. BENEFITS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EFlexible vacation \u002B 11 company holidays\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWeekly no-meeting days \u002B quarterly focus weeks\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ECompetitive health, dental, and vision insurance, including fertility\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFSA and HSA available\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EShort-term and long-term disability insurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EGroup life insurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETravel accident insurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003E401(k)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPaid parental leave\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EProfessional Development program\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ELunch, internet, \u0026amp; ancillary equipment reimbursements\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EUK BENEFITS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003ECompetitive annual leave policy (25 days annual leave plus 8 public holidays)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWeekly no-meeting days \u002B quarterly focus weeks\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPrivate medical and dental insurance, and a vision stipend, including fertility\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EShort-term and long-term disability insurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EGroup life assurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETravel accident insurance\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMatched Pension\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPaid parental leave\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EProfessional development program\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETeam social events\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E- Lunch, cafe, \u0026amp; ancillary equipment reimbursements\u003Cbr /\u003E \u003Cbr /\u003E \u003Cbr /\u003E Mux is an Equal Opportunity employer committed to building a diverse company. 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