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.
  
  

• Strategic Program Delivery :
• Successfully deliver major initiatives across AA, ESG, CCLM, and Marketing domains, meeting timelines, budgets, and defined quality SLAs.
• Ensure proactive risk identification and mitigation to avoid delivery delays.
• Maintain full compliance with ESG reporting requirements, credit exposure governance, and marketing data privacy regulations.
• Ensure zero critical audit findings by embedding compliance checks into delivery processes.
• Deploy AI/ML solutions across domains to improve operational efficiency.
• Achieve measurable reduction in manual effort and faster turnaround times through automation.
• Improve key data quality metrics (accuracy, completeness, timeliness) across all domains.
• Implement and monitor observability tools and lineage frameworks for transparency and audit readiness.
• Strengthen relationships with senior leadership, ESG committees, marketing executives, and product owners.
• Maintain high satisfaction scores through timely communication, clear updates, and delivery of business value.
• Launch new high-impact data products or capabilities that generate measurable business value.
• Drive efficiency gains or revenue growth through creative use of data and analytics.
• Develop and upskill teams in AI/ML, streaming data, governance frameworks, and domain-specific analytics.
• Improve retention and performance ratings by fostering a culture of innovation, collaboration, and excellence.
• Regulatory & Compliance Readiness
• AI & Automation Adoption
• Data Quality & Observability
• Stakeholder Engagement & Satisfaction
• Innovation & Value Creation
• Capability Building
• 14+ years of experience in data engineering, analytics, and enterprise data platforms across on-premise (Hadoop, Cloudera) and cloud (Azure, Databricks) environments.
• Functional expertise in corporate banking data domains including trade, payments, liquidity, revenues, risk.
• Strong understanding of data governance, compliance, and quality frameworks.
• Knowledge of AI/ML applications in data engineering, predictive analytics, and automation.
• Proficiency in real-time processing (Kafka, Spark Streaming) and graph databases (Neo4j).
• Leadership & Strategy – Vision setting, roadmap definition, and squad leadership.
• Program Governance – Managing multi-domain, cross-functional initiatives.
• AI & Automation – Embedding AI/ML into business processes for efficiency and insights.
• Governance & Compliance – Establishing and enforcing architecture and quality standards.
• Stakeholder Engagement – Influencing and collaborating with executives, regulators, and business leaders.
• Technical Expertise – Guiding teams on high-performance, scalable data solutions.
• Significant leadership experience in delivering multi-domain data and analytics programs in financial services.
• Proven success in regulatory-critical program delivery (ESG, credit risk, marketing governance).
• Track record of managing diverse, cross-functional teams to deliver strategic business outcomes.