Senior Analytics Engineer, Product Analytics

Ibotta•Denver, CO

23h•$127,000 - $147,000•Hybrid

About The Position

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.

Requirements

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), decision science, or e-commerce/fintech domains
A growth mindset grounded in clear communication, continuous learning, iterative product delivery, and healthy technical debate
Applicants must be currently authorized to work in the United States on a full-time basis.

Responsibilities

Design, build, and orchestrate production data pipelines that power dashboards and automate the team's internal analytical processes
Manage the team's data infrastructure: Databricks Asset Bundles (DABs), Airflow, the ongoing Airflow-to-DABs migration, and other platform migrations
Own tracking-event source-of-truth tables and build rollup tables for new tracking and business events
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