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. 

• Lead resolution of material data-service issues and recurring control weaknesses; agree corrective actions with source owners and technical partners and verify lasting improvement. 

Role Expectations 

This is a senior manager role with substantial individual contribution. The Manager is expected to personally design or review important data models and architecture, troubleshoot complex pipeline and performance issues, and step into critical delivery when needed. At the same time, the Manager must build a durable organization through hiring, coaching, delegation, standards, performance management, and succession development. Team Leads are expected to contribute substantively to delivery as well as supervise their teams. Analyst is the corporate grade for staff performing data engineering and data integration/governance work. 

Appendix Provisional Performance Scorecard 

The following scorecard is separate from the core role description. Existing weights are provisional and require agreement after a baseline period; targets should reflect maturity, complexity, and criticality. Assess platform controls and incident response directly; identify source-data dependencies and agree shared end-to-end performance measures with product and engineering owners.