Data Engineering Leader

Role Summary

The Data Engineering Leader owns the delivery, quality, and operational health of Masco's enterprise data engineering capability. Reporting to the Enterprise Data Architect, this role leads a team of Data Engineers building and operating the ingestion, transformation, and Lakehouse solutions that power enterprise POS and adjacent commercial data. This is a hands-on technical leader who codes alongside the team, holds engineers accountable to project plans and SLAs, and provides architectural support to the Enterprise Data Architect on ingestion patterns, pipeline design, and platform decisions. The Data Engineering Leader partners closely with the BI Delivery Leader to ensure enterprise data structures and models are in place for accurate, timely analytics delivery.

What You'll Own

Engineering Team Leadership & Delivery Accountability

• Lead, coach, and manage a team of Data Engineers, including performance guidance and prioritization.

• Hold the team accountable to project plans, sprint commitments, and quality expectations.

• Coordinate onshore and offshore engineering capacity, serving as the technical lead for offshore engineering resources and the bridge back to onshore leads.

• Sequence sprint delivery against the priority roadmap and requirements set by the Enterprise Data Architect, Technical Product Owner, and Business Data Analyst.

Ingestion, Pipeline & Data Platform Build

• Own the build and operation of ingestion pipelines across retailer, HQ, and BU data sources on the Databricks and Azure data stack.

• Contribute directly as a senior engineer on critical-path pipelines, Lakehouse design, and modeling work.

• Partner with the Architect to build the ingestion side of the attribution crosswalk and master data foundations to the documented spec.

• Enforce data-validation gates for completeness, outliers, and consistency before data reaches enrichment.

Incident Management, Release Management & Operational SLAs

• Own intake, triage, and resolution of pipeline incidents and data issues raised by BU and HQ consumers.

• Own release management for engineering enhancements, requests, and projects, including development operations, sprint execution, deadlines, and delivery of the business value defined by the Business Data Analyst and Technical Product Owner.

• Establish and adhere to SLAs for incident response, resolution, and communication back to consumers.

• Own monitoring, alerting, and operational health of pipelines, credentials, and source integrations.

• Escalate issues that touch the enterprise model, masters, or attribution to the Enterprise Data Architect.

Cloud Cost & Consumption Management

• Monitor cloud storage, compute, and consumption of enterprise data platforms, and track related costs.

• Contribute to budgeting for cloud, data services, and engineering tools, informed by consumption trends and workload forecasts.

• Recommend cost optimization actions such as right-sizing, workload tuning, and storage tiering as part of ongoing platform operations.

Architecture Support & Analytics Delivery Enablement

• Serve as a delivery-side extension of the Enterprise Data Architect, advising on ingestion patterns, Lakehouse design, and platform decisions.

• Enforce enterprise standards for data engineering, integration, and data quality in all work delivered by the team.

• Work closely with the BI Delivery Leader to ensure enterprise data structures, models, and metric definitions are in place for accurate and timely analytics delivery.

• Stay connected to BU data engineering counterparts for collaboration, cross-learning, and consistent enterprise practice.

Documentation & Knowledge Management

• Establish and enforce how engineering documentation works across the team, in partnership with the Enterprise Data Architect.

• Own documentation standards for pipelines, ingestion patterns, operational runbooks, credentials management, incident response, and release management.

• Ensure engineers document changes to metrics, pipelines, and data flows as part of the definition of done.

Required Qualifications

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Data Analytics, or a related field; equivalent professional experience considered.

• Proven experience in a data engineering leadership role, including managing engineers and delivery accountability.

• Substantial hands-on experience designing and building scalable data engineering solutions on a major cloud platform, with emphasis on the Azure ecosystem.

• Experience running incident management and SLA-driven support for data pipelines.

• Experience coordinating onshore and offshore engineering delivery.

Technical Skills

• Advanced hands-on expertise with Databricks and the Azure data stack (Data Factory, Data Lake, Synapse, Analysis Services).

• Deep working knowledge of the medallion architecture (bronze / silver / gold) for structuring Lakehouse solutions.

• Understanding of and experience with Microsoft Fabric, including how it fits alongside Databricks in a modern enterprise data platform.

• Advanced SQL / T-SQL and strong ETL/ELT design and build experience.

• Strong proficiency in Python for data engineering.

• Working knowledge of distributed processing and Lakehouse principles.

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

• Strong understanding of Kimball dimensional modeling and enterprise semantic layers.

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

Skills & Competencies

• Player-coach mindset. Leads the team and still contributes directly on critical-path engineering work.

• Delivery-driven. Owns commitments, SLAs, and follow-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 BU data engineering counterparts for cross-learning and consistent practice.

• Strong communicator who can translate engineering realities for business and leadership, and architectural direction for engineers.

• Detail-oriented, self-directed, and a continuous learner on modern data engineering practices.

• Ability to lead team and manage career development for small number of direct reports.

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.

• Experience with PowerBI.

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

• Experience with RESTful API development for data acquisition.

• Familiarity with modern DataOps, Agile, or Kanban delivery practices.