Data Engineering Leader

Role Summary

The Data Engineering Leader owns the delivery quality and operational health of Mascos enterprise data engineering capability. Reporting to the Enterprise Data Architect this role leads a team of Data Engineers building andoperatingthe 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 foraccuratetimelyanalyticsdelivery.

What Youll Own

Engineering Team Leadership & Delivery Accountability

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

• Hold the team accountabletoproject 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.

• Contributeto 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 foraccurateandtimelyanalytics delivery.

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

Documentation & Knowledge Management

• Establish andenforce howengineeringdocumentation worksacross 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

• Bachelors orMasters degree in Computer Science Engineering Data Analytics or a related field; equivalent professional experience considered.

• Proven experience ina dataengineering 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-onexpertisewithDatabricksand theAzure data stack(Data Factory Data Lake Synapse Analysis Services).

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

• Understanding ofand experience withMicrosoft 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.

• Strongproficiencyin 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 ofdata governance security and compliance as they apply to enterprise data engineering.

Skills & Competencies

• Player-coach mindset. Leads the team and still contributes directlyoncritical-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 leadteamand manage career development forsmallnumber 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 withPowerBI.

• Familiarity with ML/AI integration (MLflow Azure ML) andDataOps/MLOpspractices.

• Experience with RESTful API development for data acquisition.

• Familiarity with modernDataOps Agile or Kanban delivery practices.