Senior Data & Analytics Engineer, Domain Enablement (R5536)

The Senior Data & Analytics Engineer is a hybrid builder role focused on enabling business domains onto the Databricks platform by developing governed Silver and Gold assets, reusable semantic patterns, and domain-ready analytical models. This role sits between pure data engineering and pure analytics engineering: it requires enough technical depth to work comfortably with transformations and lakehouse patterns, and enough business fluency to build trustworthy models that business stakeholders can use and extend. Initial focus for this role is expected to be G&A and GTM-oriented domains such as Accounting, Program Finance, RevOps, Marketing, HR, and adjacent business functions, while remaining flexible enough to support more complex future domains such as Product or Engineering as the team matures. This role is not a dashboard factory; it is responsible for durable, governed datasets and semantic assets that accelerate domain self-service while maintaining enterprise consistency.

What you'll do:

• Build and maintain Silver and Gold data models, domain marts, curated datasets, and semantic assets for priority domains onboarding to Databricks.
• Partner directly with business stakeholders to translate domain requirements and KPI definitions into governed, testable, and reusable transformation logic.
• Apply enterprise modeling standards, naming conventions, semantic definitions, and promotion rules, contributing practical improvements back into those standards.
• Create reusable domain patterns and analytical building blocks that allow teams such as FP&A, RevOps, and Marketing to operate more self-service over time.
• Support the design of semantic views and curated layers that can be consumed by BI tools, Databricks SQL, and Genie or related AI/BI experiences.
• Work across domain boundaries when metrics overlap or interact, especially where G&A, GTM, workforce, and product-adjacent concepts intersect.
• Ensure data sensitivity, classification, and approved use are reflected in modeling choices, joins, and semantic exposure, particularly for regulated or restricted datasets.
• Review and refine partner-delivered or domain-contributed data models to ensure they are production-worthy, understandable, and aligned with enterprise definitions.
• Help domain teams grow into more self-service analytics by providing patterns, documentation, examples, and technical guidance rather than permanently centralizing every request.