Data Engineer

About this role

Job Description:
Job Title: Data Engineer
Location: Remote | Canada
Reports to: Sr. Manager - Data Engineering & Data Science
Employment Type: Full-time
This is an existing vacancy
About the Role:

JD Power is looking for a data engineer with excellent quantitative and analytical skills to help make our data AI-ready. Our ideal candidates are determined to use their skills to combine multiple data sources together, and to add clear business meaning on top of them, so that both people and AI applications can answer complex client questions around vehicle transactions, incentives, valuations, market readiness, and customer purchase decisions. You’ll work hands on with data on Snowflake to build semantic views, governed metrics, and context layers that power AI assistants and agents, and to improve and maintain existing capabilities.
We make data products that solve problems for the auto industry. Every data engineer will work on projects that go into production and directly impact new and existing products. In this role, your work decides whether our AI products give accurate, traceable answers instead of guesses.
Grow as a data engineer as part of a medium sized team, where data engineering and AI engineering meet. You will build data pipelines, semantic views, and context layers on Snowflake, deploy them in the cloud, and build monitoring & QA suites, including tests for AI answer quality.
The team is an experienced set of creative thinkers who are uncovering new and emerging consumer dynamics in an industry starting to go through rapid disruption. We are moving our key data to Snowflake and making it AI-ready, so our AI assistants and agents can give clients accurate, trusted answers.
We want data engineer candidates who can monitor, improve, and create data processes independently and with an eye toward continuous improvement. We need the capability to leverage the right data sets efficiently from an impressive set of data assets that is unrivaled in this space. We also want someone who is curious about AI, and who knows that AI is only as good as the data and business context behind it.
In this role, you will:

• Daily immersion in automotive industry data

• Build and maintain the business context that makes data AI-ready: business terms, glossaries, metric definitions, rules, and hierarchies for a data domain (e.g., incentives, pricing, sales, dealers), reviewed and approved by business owners

• Design, build, validate, and publish governed Snowflake semantic views and a versioned metric registry, so every application and AI agent uses one approved definition of each metric

• Help build canonical “master” data sets where multiple sources of truth exist today (e.g., master vehicle/VIN, master dealer, master incentives)

• Prepare unstructured content (PDF documents, glossaries, FAQs, data dictionaries) for retrieval with Snowflake Cortex Search, with metadata and source traceability

• Partner with AI engineers to connect semantic views, metrics, and search services to Cortex Analyst and Cortex Agents

• Design, troubleshoot, and execute repeatable data pipelines on Snowflake (and on our current Google Cloud/BigQuery platform during the transition), in which you’re finding bottlenecks and ways to become incrementally more efficient. You own the reliability of these pipelines and the quality of the data they produce

• Collaborate with stakeholders to gather and understand data processing requirements, with consistent proactive demonstration of business acumen and market dynamics

• Contribute meaningfully to new product development, on top of production, by executing independently to plans (and by influencing the plans themselves) for new features on existing products, and for net-new products or approaches

• Lead end-to-end data engineering projects, and mentor and guide junior data engineers

• Interdisciplinary work: you’ll interact with multiple teams across the firm to implement solutions and improve processes. This includes Customer Success, Product Management, Product Development, Technology/Architecture, Data Engineering, Data Science, AI Engineering, Data Visualization, etc.

Required Qualifications:

• Must have: strong, hands-on proficiency in both Python and SQL. You use both every day to build, test, and debug production data pipelines

• Must have: proven production experience in designing, building, and running ETL/ELT pipelines that bring together data from multiple sources, with ownership of their robustness and output data quality (e.g., idempotent jobs, monitoring and alerting, automated data quality checks)

• Software engineering background or experience is a plus: Git and code review, automated testing, CI/CD, and modular, reusable code

• Hands-on experience with Snowflake is strongly preferred (e.g., Snowpark, Dynamic Tables, role-based access control), and semantic views or Cortex experience is a big plus. Experience with other cloud platforms such as Google Cloud (BigQuery), AWS, or Palantir is also valued

• Experience building semantic layers or governed metrics (e.g., Snowflake semantic views, dbt Semantic Layer, LookML, AtScale, Cube), with a strong base in dimensional modeling (facts, dimensions, grain)

• Working knowledge of how AI applications use data: retrieval-augmented generation (RAG), embeddings and vector search, context and prompt design, tool calling, AI agents, and MCP. Hands-on experience building an LLM application is a plus

• Familiarity with ontologies, knowledge graphs, data catalogs, metadata management, and data governance (e.g., access control, PII handling, lineage)

• Familiar with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot), with hands-on experience using them in daily development work. You know their limits, and you review and test the code they produce before it goes to production

• Experience resolving data issues in creative and effective ways. Business acumen and gut instincts to know when to say,…