Senior Data Engineer

Ibotta•Denver, CO

6d•$144,000 - $165,000•Hybrid

About The Position

Ibotta is seeking a software-focused Senior Data Engineer to join their Data Platform Organization and contribute to the newly formed Data Intelligence Squad. The role focuses on making Ibotta's data AI-ready by working on data contracts, metadata standards, compliance tooling, and pipelines that feed an AI readiness scoring system. The position will also involve LLM application development and semantic layer work as the team evolves. The ideal candidate is a highly motivated senior data engineer who values clean, well-governed data, understanding its importance in an AI-driven environment.

Requirements

5+ years of software engineering experience with a focus on data engineering, analytics engineering, or backend development.
Demonstrated ability to lead complex, multi-stakeholder data initiatives from scoping through delivery; experience writing technical design documents that clearly define the problem, present alternatives, and propose a solution aligned with iterative value delivery.
Expertise in Python, SQL, and Databricks, and experience with medallion architecture (bronze/silver/gold) or similar layered data design patterns.
Independently design and implement semantic layers to ensure AI agents utilize governed, consistent metric definitions rather than querying raw tables directly.
Experience working in data governance concepts (metadata standards, data ownership, schema enforcement, access control) and experience keeping data assets clean and well-documented.
Confidence working with schema validation, data freshness monitoring, data observability tooling, or similar quality practices.
Demonstrated ability to evaluate and integrate AI into the solution tech stack, including understanding operational tradeoffs (token cost, latency, build vs. buy); hands-on experience with AI building blocks such as LLM APIs, context engineering, agentic frameworks, and RAG; proficiency in AI-assisted development platforms (Claude, Copilot, Cursor, etc.) a plus.
Bachelor's degree in Computer Science, Engineering, or a related field.
Applicants must be currently authorized to work in the United States on a full-time basis.

Nice To Haves

Experience with data catalog, metadata management, or data transformation tooling.
Exposure to data privacy, classification, or compliance requirements in a data context.
Familiarity with configuration-as-code or automated deployment workflows.
Experience with data observability or data quality monitoring.
Agile development experience.

Responsibilities

Lead the design and rollout of data contract standards that codify table ownership, schema expectations, and quality thresholds; enforce standards through automated validation in the deployment pipeline; collaborate with data owners to improve metadata coverage across the team's data estate.
Own the end-to-end architecture and delivery of the automated scoring pipeline that evaluates data assets on their readiness for AI and analytical use; build and maintain the jobs that collect quality signals and surface scores to data teams.
Drive adoption of governance frameworks across the data estate; maintain tagging and lineage automation frameworks; help translate data access and privacy policies into practical guardrails in partnership with the platform team; monitor AI usage across the company, defining new avenues for AI assistance.
Build the systems that make people actually trust what the AI tells them about our data, such as query accuracy checks, guardrails that keep AI tools on certified data, and clear attribution so users can see where an answer came from; make sure we have the monitoring in place to catch problems before they erode confidence.
Build and maintain the semantic framework that make data assets more legible to both people and AI systems, adding descriptions, context, and query guidance that improve the accuracy of natural language queries; work with analysts and domain experts to capture and formalize business definitions.
Proactively leverage AI tools (e.g., Claude, Databricks Genie) to accelerate development, maintain code quality, and explore new approaches to data engineering problems.
Drive alignment with adjacent teams to onboard data assets into the AI-ready platform; influence roadmap priorities based on data readiness gaps; document processes and contribute to the team's shared knowledge base.
Tech Lead squad initiatives end-to-end; take responsibility for the growth and development of junior and mid-level engineers, not just as a teacher but as someone who actively invests in their careers; drive architectural decisions and represent the squad in cross-team design reviews.
Embrace and uphold Ibotta's Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency, and A good idea can come from anywhere.