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Ibotta is seeking a Senior Analytics Engineer to serve as the technical backbone of our Product Analytics team in our mission to Make Every Purchase Rewarding.   In this senior, high-ownership role, you will build scalable pipelines, set engineering standards, and establish the data foundations that power product decisions across Ibotta. Operating horizontally across diverse domains; from consumer products, to client surfaces, to semantic layers; you will elevate data quality, empower decision scientists and data scientists, and enable self-service analytics at scale.   This position is located in Denver, Colorado as a hybrid position requiring 3 days in office (Tuesday, Wednesday, and Thursday). Candidates must live in the United States.   Not based in Denver? We will offer a relocation bonus to help make your move to the Mile High City a smooth one.   What you will be doing: - Design, build, and orchestrate production data pipelines that power dashboards and automate the team's internal analytical processes; the core, front-and-center focus of this role - Manage the team's data infrastructure: Databricks Asset Bundles (DABs), Airflow, the ongoing Airflow-to-DABs migration, and other platform migrations; infra support is available, but ideally you can operate independently - Own tracking-event source-of-truth tables and build rollup tables for new tracking and business events (the team owns all tracking events; some need rollups, some are good as-is) - Implement and utilize engineering best practices and methods to provide quality curated data sets - Stand up CI/CD (GitHub Actions) and automated testing for analytics code and pipelines - Own data quality, reliability, and governance: schema validation, data contracts, anomaly detection (Monte Carlo or processes you build yourself) to catch issues early, plus PI discovery, classification, and tagging - Optimize and aggregate raw data into a consumable format for downstream users in Decision Science and Business Intelligence - Build and maintain Looker / LookML dashboards, some of which our pipelines rely on for orchestration - Set patterns and examples for a largely self-service team, and actively coach and mentor newer members on best practices and engineering standards - Manage stakeholders across the org: connect the dots, sequence competing requests, and know when to wait for readiness versus push ahead to enable the team - Collaborate with Data Engineering to streamline our ETL processes and warehouse environment - Work cross-functionally across the entire organization to democratize our data strategy and foster data literacy - Integrate AI into engineering workflows. We build Claude skills and plugins to automate, standardize, and assist our day-to-day work and enable the team to gain speed and accuracy. - Monitor, maintain, and modernize existing data services - Embrace and uphold Ibotta’s Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency & A good idea can come from anywhere   What we are looking for: - 5+ years in Analytics, Data, or Software Engineering - Bachelor’s degree in Computer Science, Analytics, Statistics, Economics or related field required - Hands-on experience building production pipelines using Python, expert SQL, Spark, Airflow, and cloud data warehouses (Databricks preferred) - Demonstrated practice with automated testing, CI/CD (GitHub Enterprise/Actions), data validation, and anomaly detection - Proven ability to establish self-service patterns, set engineering standards, mentor team members, and manage cross-functional priorities in an Agile environment - Direct proficiency across Databricks (including DABs), Looker / LookML, Airflow, and GitHub Enterprise for rapid autonom - Experience with semantic layers, data contracts, event-driven architecture, and AI-assisted tooling (Claude, Cursor, Copilot, Databricks Genie) - Exposure to product experimentation (A/B testing)