Data Engineering and Platform Manager

Data Engineering and Platform Manager Data Pipelines Platforms Quality and Reliability Reports to: Chief Data and Analytics Officer Department: IDEA — Intelligence, Data, Engineering & Analytics Team structure: Two Team Leads and four Analysts across Data Platform Engineering and Data Integration & Governance Position Summary The Data Engineering and Platform Manager is the senior hands-on leader for WBL's analytical data foundation on Azure and Databricks. Reporting to the CDAO, this role manages through two Team Leads, each responsible for two Analysts. Data Platform Engineering builds and operates the lakehouse, pipelines, data models, and platform services; Data Integration & Governance owns analytical ingestion, data quality controls, master/reference data capabilities, and lineage. The Manager sets architecture and engineering standards, translates business and product needs into scalable data capabilities, and remains technically engaged in the design and resolution of high-impact work. The role is accountable for a platform that is reliable, secure, cost-disciplined, well documented, and capable of supporting fast business endpoints and analytical products at scale. Core Responsibilities Lead Data Engineering Teams Manage, coach, and develop two Team Leads and four Analysts, with clear ownership, technical standards, feedback, and accountability. Set team priorities, allocate capacity, remove delivery blockers, and maintain appropriate operational coverage for critical data services. Own the platform roadmap and resource plan; hire, assess performance, develop Team Leads, and build succession coverage for critical data capabilities. Maintain regular hands-on involvement in priority delivery. Build and Operate the Data Platform Oversee data ingestion, transformation, orchestration, storage, and delivery from design through production support. Work backward from business endpoints and product requirements to define business-ready data models, schemas, freshness, and performance requirements. Maintain platform availability, performance, monitoring, scalability, and cost discipline, including incident response and root-cause follow-up. Set the target data architecture and modernization priorities; translate product needs into delivery commitments and balance capacity, performance, resilience, and platform cost. Govern Data Quality and Security Set standards for data architecture, master/reference data, testing, deployment, documentation, lineage, access, retention, and change control across the analytical platform. Partner with analytics, intelligence products, security, infrastructure, and business owners to provide dependable governed data; business owners define source meaning and own source-process corrections. Own analytical-platform ingestion and data delivery; Software Engineering owns operational application integrations. Agree interface contracts and incident routing at shared boundaries.…