Data Engineering and Platform Manager

World Business Lenders

22h•Remote

About The Position

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.

Requirements

Twelve or more years of progressive experience in data engineering, data platforms, or data architecture, including at least five years of people leadership and meaningful experience leading Team Leads, managers, or senior technical staff.
Demonstrated success building, scaling, or materially modernizing a production data platform or data engineering function.
Must be able to coach Team Leads, develop senior technical talent, allocate capacity, establish engineering standards, manage incidents and operational risk, and make roadmap and prioritization decisions with senior business leaders.
Recent hands-on technical delivery is required; this is not a management-only role.
Experience designing business-ready curated or gold-layer datasets by working backward from downstream products, calculators, APIs, reporting, or decision-support requirements rather than treating ingestion as the endpoint.
Experience operating data platforms with large datasets and demanding performance requirements, including query and data-model optimization, partitioning or clustering strategies, caching, and other techniques used to support low-latency analytical workloads.
Experience integrating data from operational systems, third-party vendors, APIs, files, and batch feeds while managing data contracts, schema changes, reconciliation, lineage, and source-quality issues.
Experience establishing pragmatic data governance inside an engineering organization, including ownership, data-quality controls, metadata/documentation, lineage, access, retention, and production change controls.
Deep practical experience with SQL, dimensional and analytical data modeling, ETL/ELT, orchestration, APIs, cloud storage and compute, automated testing, CI/CD, observability, monitoring, and incident management.
Strong practical ability to design and lead delivery in an Azure and Databricks environment, including lakehouse patterns, workload performance, reliability, and cost optimization.
Strong understanding of data quality, lineage, metadata, master/reference data, access control, security, retention, schema evolution, change management, performance engineering, and cloud cost management.
Able to translate between business requirements, product requirements, and technical architecture and to explain trade-offs clearly to both executives and engineers.

Nice To Haves

Relevant education or professional training in computer science, engineering, information systems, data, or a related discipline is valued. Demonstrated technical depth, leadership, and production delivery experience are the primary qualifications; a degree is not mandatory.
Financial-services, lending, credit, portfolio, or other data-intensive regulated-industry experience is helpful but not required.
Experience in lending or financial services, business-user support, and data reconciliation preferred.

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.