Senior Analytics Engineer (Data Platform & AI Enablement)

Masco Corporation•Indianapolis, IN

1d•$88,700 - $139,260

About The Position

The Senior Analytics Engineer owns the layer between curated enterprise data and business consumption. Working primarily in Databricks, Unity Catalog, and Power BI, this role designs and maintains certified data products, semantic models, standardized metrics, and quality controls used by analysts, self-service users, dashboards, and AI agents. This role replaces a traditional Data Engineer position. Its primary accountability is analytics readiness and trust, rather than source ingestion or ETL throughput. This role creates a common, governed foundation for reporting, analysis, self-service, and AI. It moves repeat logic out of individual reports and into reusable certified assets, reducing conflicting metrics, duplicate datasets, and analyst preparation effort. It enables the Decision Intelligence value stream from Intake to Insight to Impact.

Requirements

Bachelor's degree in a quantitative or technology field, or equivalent practical experience.
5+ years in analytics engineering, data engineering, BI engineering, or data platform roles, including experience with curated analytics layers or semantic models.
Advanced SQL and strong dimensional modeling skills, including fact grain, conformed dimensions, and star schemas.
Production experience with a cloud Lakehouse or warehouse; Databricks and Delta Lake strongly preferred.
Power BI semantic modeling experience, including DAX, relationships, security, and optimization.
Experience with data governance, metadata, lineage, quality controls, Git, code review, and deployment discipline.
Ability to translate business questions and metric definitions into scalable data products and plain-language documentation.

Nice To Haves

POS, omnichannel retail, consumer products, or manufacturing data.
Dataset certification, data product management, dbt or Lakeflow/DLT, and Databricks Workflows.
Preparing governed data for AI agents, RAG, forecasting, or advanced analytics.

Responsibilities

Build and maintain certified facts, conformed dimensions, curated views, and Gold-layer data products.
Own Power BI semantic models, shared measures, hierarchies, relationships, security, and performance.
Translate agreed business rules and KPI definitions into transparent, testable, version-controlled logic.
Implement Unity Catalog governance, metadata, access controls, lineage, certification, and deprecation standards.
Automate data quality, reconciliation, freshness, and anomaly monitoring for certified products.
Publish reusable, documented assets that support self-service analytics and governed AI consumption.