Lead Analytics Engineer

chamberlain; Oak Brook (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Chamberlain Group in Oak Brook, IL seeks a Lead Analytics Engineer to set standards for curated data models, metrics, and self-service analytics across the organization. This hands-on leadership role owns the Gold-layer design, builds domain data marts, and mentors engineers and analysts.

You will partner with data engineers and business partners, define governed metric views, implement tests, and deliver dashboards for decision-makers while controlling performance and cost.

Qualifications

• Experience building curated data models and enterprise metrics.
• Strong SQL, Python, and data engineering fundamentals.
• Familiarity with Databricks, Unity Catalog, and BI tooling.

Responsibilities

• Design and maintain Gold-layer data models and domain data marts.
• Define enterprise metrics as governed Unity Catalog views.
• Develop modular transformation logic in Databricks SQL and PySpark with tests.
• Design, build, and maintain Databricks AI/BI dashboards.
• Partner with business stakeholders to translate questions into durable data models.
• Serve as technical lead for analytics engineering and mentor engineers.
• Establish data contracts with Data Engineering and upstream producers.
• Produce documentation, lineage, and metadata for self-service.
• Advance data literacy and self-service through enablement materials.
• Apply software engineering discipline to analytics code and CI/CD deployments.

Skills

SQL

Python

Databricks SQL

PySpark

Data modeling

Git

CI/CD

Tools

Databricks

Unity Catalog

BI dashboards

Job description

Chamberlain Group (CG) is a global leader in intelligent access and Blackstone portfolio company. Powered by our myQ technology, we make access simple and secure for millions of homeowners, businesses, and communities worldwide. Our flagship brands, LiftMaster® and Chamberlain® , are found in 51+ million homes, and 14 million+ people rely on the myQ® app daily.

Job Summary

This role is a senior individual contributor in analytics engineering at Chamberlain Group and sets the technical standard for how curated, trusted data is modeled and published for enterprise-wide consumption. Where our Data Engineers are accountable for reliably landing and conforming data on the Databricks platform, the Lead Analytics Engineer is accountable for what happens next: designing the curated Gold-layer data models and domain data marts in Unity Catalog, defining enterprise business metrics once as governed Unity Catalog metric views, building the dashboards that put those metrics in front of decision-makers, and making that layer well-tested, well-documented, performant, and genuinely self-serviceable. This is a hands-on technical leadership role without direct reports. This role will establish modeling and metric standards, reviewing the work of other Analytics Engineers and external partner resources, and mentoring engineers and analysts across the organization. Success looks like a single agreed definition for every enterprise metric, business partners who trust the numbers and stop building shadow reports, and an analytics layer that serves BI tools, SQL users, and AI agents consistently.

Essential Duties and Responsibilities

• Design, build, and maintain curated Gold-layer data models and domain data marts in Unity Catalog, applying dimensional modeling practices - star schema design, grain decisions, slowly changing dimensions - and conforming dimensions across domains so function-specific marts reconcile enterprise-wide.
• Define and own enterprise business metrics as governed Unity Catalog metric views, so each metric is defined once and returns a consistent answer across SQL, dashboards, external BI tools, and AI agents.
• Develop modular, idempotent transformation logic in Databricks SQL and PySpark - materialized views, streaming tables, and declarative pipelines - with data quality and validation tests (uniqueness, referential integrity, freshness, business-rule assertions) built into every published model.
• Design, build, and maintain Databricks AI/BI dashboards that make enterprise metrics visible and actionable; rationalize and retire redundant or conflicting reports by migrating their logic into governed models.
• Partner with business stakeholders to translate ambiguous questions into durable data models and dashboards, facilitate agreement on contested metric definitions, and govern change to established metrics - versioning definitions, assessing downstream impact, and communicating clearly why reported numbers move.
• Serve as technical lead for the analytics engineering discipline: establish modeling, naming, testing, and metric standards; conduct design and code reviews for Chamberlain Group and partner engineers; and mentor engineers and analysts to raise the team's SQL, Python, and modeling capability.
• Establish data contracts with Data Engineering and upstream data producers - defining the fields, grain, timeliness, and semantics the analytics layer depends on - so source system changes are anticipated rather than discovered as breakage.
• Produce and maintain documentation, column-level lineage, and business-friendly, agent-ready metadata (business definitions, synonyms, display names, formatting rules), and curate natural-language query experiences - scoped tables, instructions, example queries, and accuracy benchmarks - so business users get trustworthy self-service answers.
• Advance data literacy and self-service by onboarding analysts and business users to the curated and semantic layers, building enablement material, and holding office hours and working sessions.
• Apply software engineering discipline to analytics code - Git-based version control, pull request review, automated testing, and CI/CD deployment via Declarative Automation Bundles - and implement data protection controls (classification, row filters, column masks) per governance and platform standards.
• Use AI-assisted development tools and agentic coding workflows to accelerate model development, testing, documentation, and migration of legacy report logic, and build reusable AI skills, prompts, and agent configurations that encode CG's modeling standards - versioned, documented, and held to the same review and testing bar as any other change.
• Lead analytics engineering projects from requirements through adoption, measuring whether delivered models and dashboards are used, while tuning performance and managing platform consumption cost (materialization strategy, incremental processing, liquid clustering, query optimization).
• Comply with health a