Technical Delivery Manager - Data Lake

This role requires strong technical knowledge of enterprise data platforms, excellent stakeholder management skills, and proven experience delivering complex projects within the banking and financial services industry.

Requirements

Demand & Delivery

· Manage end-to-end delivery for Data Lake / EDA initiatives.

· Drive project intake, demand assessment, and prioritization.

· Track scope, schedule, budget, risks, and dependencies.

· Ensure delivery commitments are met across projects and BAU enhancements.

Technical Solution Delivery:

· Ensure compliance with EDA onboarding principles.

· Drive data discovery activities with source application owners.

· Validate completeness of source scope (tables, files, attributes, interfaces).

· Challenge assumptions and ensure onboarding is not limited by downstream use cases.

Stakeholder Engagement:

· Act as the primary interface between - Business teams, Project Managers, Source Application Teams, Data Engineers, Solution Architects, Infrastructure / Platform Teams, Operations and Support Teams, facilitate discussions, drive decisions, and resolve delivery bottlenecks. Manage escalations proactively.

Risk Management:

· Drive closure of audit, risk, and compliance findings, ensure onboarding complies with data governance standards. Monitor platform obsolescence, remediation activities, and operational risks. Coordinate ITC, architecture, security, and governance approvals.

Resource Allocation:

· Plan resource allocation across multiple projects to allocate resources effectively to support project activities, including assigning tasks, managing workloads, and optimizing team productivity.

Delivery & Project Management: End-to-end project delivery management, Managing multiple concurrent projects and BAU initiatives, Scope, schedule, budget, and dependency management, governance milestone management

Stakeholder Management: Ability to engage business, project teams, architects, and source application owners, Strong negotiation and influencing skills, Escalation management, managing senior stakeholders and steering committees, Driving accountability across teams without direct authority

Data & Integration Knowledge: Data Lake / Data Warehouse concepts, Data onboarding and discovery, Processes, ETL / ELT fundamentals, Batch and real-time integration patterns, Data lineage, metadata, and data quality concepts, Understanding of source-to-target data flows

Governance & Compliance: Data governance principles, Change and release governance, Regulatory and audit awareness

Technical Understanding: Possess sufficient technical depth to assess and challenge source system architecture, data models and source-to-target mappings, Batch and real-time ingestion design, Hadoop / Spark / Kafka ecosystems, Cloud data platforms, Production support and operational requirements -Not necessarily hands-on, but sufficiently technical to challenge assumptions and assess impact.