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# Senior Data Engineer

- **Company:** Mux, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Amazon Web Services, Backup Devices, BigQuery, Business Systems, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Cursor, Programming Tools, Dimensional Modeling, Python (Programming Language), Netsuite, Operational Databases, Performance Tuning, Redis, Salesforce.Com, Software Engineering, SQL Databases, Systems Integration, Macros, Large Language Models, Claude Code, Snowflake, Caching, AI Coding Agents, Data Layers, Apache Kafka, Data Management, Video Streaming, Hubspot, Api Design, Cloud Optimization, Stream Processing, Code Restructuring, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8a84bf8ced710625

## About the Role

* 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, * 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

## Description

* 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, * 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