Lead Analytics Engineer

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, Lift Master® and Chamberlain®, are found in 51+ million homes, and 14 million+ people rely on the myQ® app daily.

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.

• 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…