Data Engineer Injaz - TECH - Digital Data Platforms 41 MIT

Data & Analytics Driven Organization: One Data Analytics & AI Function drive the vision to transform the Bank into a Data Driven Organization (DDO) using latest tools and technologies in Data space.
To lead and manage multiple data and analytics squads — Advanced Analytics (AA), Environmental, Social & Governance (ESG), Corporate Credit & Limit Management (CCLM) Data, and Marketing Campaign Data & Analytics — ensuring delivery of scalable, compliant, and business-aligned solutions. The role is accountable for defining the strategic roadmap, driving AI-enabled innovation, embedding strong governance and data quality frameworks, and enabling data-driven decision-making across corporate banking. As the primary link between business leadership, regulatory bodies, and technology teams, the Cluster Lead ensures high-impact outcomes that support revenue growth, operational efficiency, ESG compliance, and enhanced customer experience
Key Responsibilities:.
- Cluster Leadership & Strategic Direction: Lead multiple squads across Advanced Analytics (AA), ESG Data, and Corporate Credit & Limit Management (CCLM) Data, CIBG Marketing Campaign, ensuring seamless delivery of high-impact, enterprise-scale programs.
- Define and execute the cluster’s strategic roadmap, ensuring alignment with corporate banking goals, enterprise data strategy, and regulatory priorities.
- Digital Transformation Programs: Oversee the execution of strategic programs for corporate digital banking across regions covering solutions for Trade Finance, Liquidity Management, Payment Systems, and Collections.
- Analytics and Business Intelligence: Deliver end-to-end BI solutions and business operational reports that support decision-making at the executive level, with a focus on timeliness, accuracy, and actionable insights across regions.
- Data Engineering and Technology Leadership: Oversee data architecture, big data engineering, and AI-driven models to unlock new insights and address complex business challenges.
- Cross-Functional Collaboration: Coordinate with core banking, risk, compliance, and digital transformation teams to create and maintain a unified, agile, and scalable data model that supports GTB.
Stakeholder and Resource Management: Manage project timelines, resources, and deliverables while ensuring a seamless execution of business-critical data projects.