Sr. Manager, Data Foundations

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.

Job Summary This role leads the delivery and day-to-day operation of Chamberlain Group's data engineering, analytics engineering, data quality, and Databricks platform capabilities within the Data and Analytics Foundations group. This hands-on leader manages a blended team of full-time Data Engineers and Analytics Engineers alongside external delivery partners, translating the data foundations roadmap into working pipelines, trusted data products, and a well-governed, cost-efficient lakehouse platform.

Success in this role looks like reliable data delivered on predictable timelines, measurable improvement in data quality and platform efficiency, engineers who are growing in their craft, and business stakeholders who trust and increasingly self-serve from the data we publish.

Essential Duties and Responsibilities Lead the design, delivery, and operation of scalable batch and streaming pipelines, curated data products, Databricks AI/BI dashboards, and natural-language query experiences over governed data, personally engaging in solution design, code review, and hands-on development where it accelerates the team.

Manage the day-to-day delivery of a blended team of full-time Data Engineers, Analytics Engineers, and external partner resources — setting priorities, sequencing intake, removing blockers, planning capacity, accepting deliverables, and holding partners accountable to agreed quality, throughput, and responsiveness expectations.

Own the Databricks platform operationally, including Unity Catalog administration, workspace and compute configuration, access provisioning and least-privilege enforcement, secrets management, and cost attribution and consumption optimization.

Set the standards for data classification, masking, and access control that engineers implement on published data, in partnership with Data Governance and Information Security, and verify adherence through review.

Own the quality and coherence of the curated and semantic layer the team publishes — Gold-layer models, enterprise metric definitions, certified datasets, and dashboards — holding the Analytics Engineering discipline to agreed standards and arbitrating metric definition disputes that cannot be settled at the working level.

Establish data quality and observability practices so every pipeline ships with quality rules, monitoring, alerting, lineage, and privacy classification by design, and own production reliability for the data estate, including incident response, root cause analysis, and adherence to agreed service levels.

Engage, align, motivate, and inspire the team to maximize their impact, raising the technical bar through mentoring, pairing, and code review.

Drive adoption of and adherence to engineering standards across full-time and partner engineers alike — version control and pull request practices, automated testing, CI/CD, infrastructure and pipeline-as-code, definition of done, and documentation and runbook standards that reduce key-person risk.

Champion the practical adoption of AI-assisted development tools and agentic workflows across full-time and partner engineers to accelerate development, testing, documentation, and migration; set the expectation that AI-generated code is reviewed and tested to the same standard as human-written code, establish guardrails for data privacy and secrets handling, and sponsor reusable AI assets — shared…