Role 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. 

What You'll Own 

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. 

Required Qualifications 

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. 

• Advanced SQL / T-SQL (queries, stored procedures, functions, indexes, partitions, DDL, DML) and strong ETL/ELT design and build experience. 

• Strong proficiency in Python (or Scala) for data engineering. 

• Experience with RESTful API development for data acquisition and ingestion from retailer and third-party sources. 

• Working knowledge of distributed processing (Apache Spark) and Lakehouse principles — ACID storage, schema enforcement, versioning. 

• Solid understanding of CI/CD, DevOps, and automation for data workflows. 

• Strong understanding of Kimball dimensional modeling, including snapshot/transactional fact tables and conforming dimensions. 

• Working knowledge of data constructs including keys, constraints, data types, normalization, referential integrity, and data quality analysis. 

• Understanding of data governance, security, and compliance as they apply to data engineering. 

Skills & Competencies 

• Detail-oriented and thorough. Considers all aspects of a data problem, including how the small pieces relate to the larger picture. 

• Highly self-motivated and self-directed. Operates with minimal oversight on complex, hands-on data work. 

• Delivery-focused. Owns commitments and follows through. 

• Willingness to explore and understand new and modern data tools to add value to the enterprise POS and POS-related engineering space. 

• Collaborative with the Data Engineering Leader, Enterprise Data Architect, and BI Developers on shared pipelines, models, and metric definitions. 

• Continuous learner who stays current on modern data engineering practices and tools. 

• Strong communicator who can explain technical detail clearly to both engineering and analytics peers. 

Preferred Qualifications 

• Experience in retail, consumer goods, or manufacturing analytics environments where POS, sell-in, inventory, and third-party retail data are core. 

• Experience with Databricks Unity Catalog, data lineage, and observability tools. 

• Familiarity with ML/AI integration (MLflow, Azure ML) and DataOps/MLOps practices. 

• Experience with Microsoft Tabular models and the DAX language. 

• Experience with at least one data analysis tool such as Power BI, Tableau, etc.