Lead Analytics Engineer

Chamberlain Group•Oak Brook, IL

2d•$102,600 - $193,425•Hybrid

About The Position

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.

Requirements

Bachelor's Degree in computer science or equivalent relevant work experience.
4+ years of experience in analytics engineering, data engineering, data warehousing, or business intelligence development, with a majority spent building production data consumed by business users, including 2+ years hands-on with Databricks
Demonstrated technical leadership, including responsibility for standards, design review, and code review of other engineers' work
Demonstrated experience owning enterprise or cross-functional metric definitions and driving alignment among stakeholders with competing definitions, including managing changes to established definitions and the restatement of previously reported numbers
Experience designing and building dashboards used by business and executive audiences, and improving or retiring an existing reporting estate
Demonstrated hands-on use of AI development tools such as Claude, GitHub Copilot, or Databricks Genie Code to improve the speed and quality of development or analytics work, with the ability to articulate specifically where those tools helped and where they did not
Expert SQL, including window functions, complex joins, common table expressions, and incremental and idempotent transformation patterns
Strong Python development skills, including PySpark, applied to building production transformations, automation, testing, and reusable tooling — this role requires genuine proficiency in both SQL and Python, not SQL alone
Demonstrated data modeling expertise, including dimensional and star schema design, domain data mart design, grain and conformance decisions, and slowly changing dimensions
Significant hands-on experience with Databricks, including Delta Lake, Unity Catalog, Databricks SQL, and medallion architecture, and with publishing curated data from the lakehouse for analytical consumption
Dashboard and data visualization skill, including chart selection, layout, and performance, with judgment about when a dashboard is the right answer and when it is not. Dashboard craft developed on another enterprise BI platform is transferable; the Databricks requirements above are not
Practical experience implementing data quality testing, documentation, and lineage as part of routine delivery rather than as an afterthought
Git-based version control and CI/CD practices applied to analytics code, including environment promotion
Ability to translate ambiguous business questions into durable data models and dashboards, including stakeholder interviewing, requirements documentation, and metric arbitration
Working understanding of data classification and access control concepts for sensitive and personally identifiable data, and the ability to implement them on published data
Ability to teach — to raise the data capability of analysts and engineers through documentation, enablement material, pairing, and working sessions
Sound judgment about where AI tooling meaningfully accelerates analytics work and where it introduces risk, with the discipline to review, test, and validate AI-generated code and metric logic rather than accepting it uncritically
Ability to influence and raise standards without formal authority, and to give and receive direct technical feedback constructively
Ability to deal with ambiguity and make quality decisions in a dynamic, fast-paced environment
Strong presentation, written and verbal communication skills with an ability to communicate effectively to all levels of the organization, including translating technical concepts for business audiences
Insistence on high standards with a strong desire to transform and improve and a bias for action

Nice To Haves

Databricks Certified Data Engineer Associate or Professional, or Databricks Certified Data Analyst Associate
Azure platform certification
Hands-on experience with Unity Catalog metric views specifically
Experience with Lakeflow Declarative Pipelines (formerly Delta Live Tables) and Lakeflow Jobs
Experience with Declarative Automation Bundles (formerly Databricks Asset Bundles) or comparable deployment-as-code tooling
Experience building reusable AI skills, prompt libraries, or agent configurations adopted by other engineers
Experience designing governed, certified datasets and semantic definitions consumed by Databricks AI/BI dashboards and other enterprise BI tools connected to the lakehouse
Experience building or curating AI/BI Genie spaces, or comparable experience enabling natural language or AI agent access to data through curated metadata, example queries, and verified assets
Experience applying agentic AI development tools, such as Claude Code or comparable tools, to analytics engineering work including legacy report logic migration, test generation, and large-scale SQL refactoring
Experience creating enablement material, patterns, or standards that helped other engineers or analysts use AI tools effectively
Experience with data observability or data quality tooling
Experience with agile/scrum development methodologies
Azure platform expertise, including one or more certifications
Familiarity with data catalog, master data management, and data governance practices
Familiarity with cost attribution and consumption optimization on a cloud data platform

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 and safety guidelines and rules; managers should also ensure compliance across their teams.
Protect Chamberlain Group’s reputation by keeping information confidential.
Maintain professional and technical knowledge by attending educational workshops, reading professional publications, establishing personal networks, and participating in professional societies.
Contribute to the team effort by accomplishing related results and participating on projects as needed.