Data Engineer

Summary   The Data Engineer designs, builds, and   operates   the ingestion, transformation, and Lakehouse solutions that power Masco's enterprise POS and adjacent commercial data. This role delivers high-quality, reliable, and secure data pipelines against the standards and specifications set by the Enterprise Data Architect and the direction of the Data Engineering Leader. The engineer contributes   across   the full pipeline lifecycle — ingestion, data quality, transformation, and enrichment — including the pipeline-side execution of the enterprise attribution crosswalk. Depending on assignment, the Data Engineer may focus more heavily on ingestion   or on   quality and pipeline development, but the role covers all aspects.

Ingestion & Pipeline Development   Design, build, and   maintain   automated ingestion pipelines and multi-source integration for   retailer , HQ, and BU data on the Databricks and Azure data stack.   Develop RESTful APIs and API-based integrations for   retailer   and third-party data   acquisition .   Deliver   critical-path   pipelines and validation for priority data feeds.   Integrate with orchestration tools and cloud or hybrid storage systems to enable end-to-end data workflows.   Contribute to Lakehouse solutions using ACID-compliant storage layers, schema enforcement, and versioning for reliable data management.   Data Quality, Validation & Monitoring   Run data-quality checks at the pipeline level — missing values, outliers, consistency, and cross-source reconciliation.   Implement monitoring, validation, and automated notifications within the data lifecycle to protect performance, quality, and availability.   Support pipeline monitoring, incident response, and continuous improvement in partnership with the Data Engineering Leader.   Maintain change control and testing processes for modifications to pipelines and data models.   Attribution & Master Data Support   Build the ingestion side of the attribution crosswalk and master data foundations against the Enterprise Data Architect's documented spec.   Support maintenance of hierarchy mappings and attribute relationships through pipeline work.   Surface data-quality issues, mapping exceptions, and structural gaps back to the Data Engineering Leader and Architect.   DevOps, Automation & Data Preparation   Implement CI/CD pipelines for data engineering workflows and automate testing, deployment, and monitoring of data solutions.   Develop,   maintain , and secure tables, relationships, metrics, and calculations for analytical models supporting reporting and advanced analytics.   Contribute to research and recommendations on data management products, services, and standards.   Documentation & Knowledge Management   Document ingestion patterns, pipelines, validation rules, and metric definitions as part of the   definition of   done.   Contribute pipeline lineage and technical metadata to the enterprise catalog.   Follow the documentation standards   established   by the Data Engineering Leader and Enterprise Data Architect.

Education & Experience   Bachelor's or   Master's degree in Computer Science , Engineering, Data Analytics, or a related field; equivalent professional experience considered.   Substantial hands-on experience designing and building data engineering solutions on a major cloud platform, with emphasis on the Azure ecosystem.   Experience delivering production pipelines and supporting them in an operational setting.   Technical Skills   Advanced hands-on   expertise   with   Databricks   and the   Azure data stack   (Data Factory, Data Lake, Synapse, Analysis Services).   Strong working knowledge of the   medallion architecture   (bronze / silver / gold) for structuring Lakehouse solutions.   Familiarity with   Microsoft Fabric   and how it fits alongside Databricks in a modern enterprise data platform.