Primary Job Responsibilities :

- Own the enterprise data platform and data lakehouse end to end, covering architecture, build, and run across the ingestion, transformation, storage, and consumption layers.

- Own the enterprise data model, ingestion standards, data quality, and lineage across all source institutions, ensuring consistent definitions and reliable, auditable data.

- Own all integrations into and out of the platform, including member institution core banking systems and regulatory and national fraud reporting systems.

- Be accountable for data availability, timeliness, and fitness for purpose across all analytics, machine learning, and management reporting use cases.

- Establish data governance and controls, covering access management, retention, encryption, and audit evidence, in line with regulatory expectations.

- Hold delivery partners and implementation vendors to committed scope, technical specification, service levels, and go-live milestones, through structured governance, milestone tracking, and timely escalation.

- Own platform performance, resilience, and cost efficiency, including capacity planning, disaster recovery readiness, and cloud cost optimisation.

- Lead and build the data engineering team, setting technical standards, code review practices, and delivery discipline, and developing capability within the team.

- Partner with analytics, product, security, and business teams to translate institutional and regulatory requirements into platform capability.

Professional Skills :

- 7+ years in data engineering or data platform roles, including experience leading a team and owning a platform in production.

- Hands-on depth in modern data architecture, including lakehouse patterns, open table formats, batch and streaming ingestion, and distributed processing with Spark or equivalent.

- Strong SQL and Python, with the ability to review and improve the team's technical output.

- Experience integrating with core banking, payment, or other transaction systems in a financial services environment.

- Experience with data quality frameworks, metadata management, lineage tooling, and transaction-level reconciliation.

- Working knowledge of cloud data services, workflow orchestration, infrastructure as code, and CI/CD for data pipelines.

- Understanding of data protection, access control, encryption, retention, and audit requirements applicable to regulated financial institutions.

- Demonstrated experience governing systems integrators and technology vendors against technical specification and delivery commitments.

- Experience establishing a data engineering function from an early stage is an advantage.

Educational Qualifications :

- B. Tech / B.E / MCA / Masters in Computer Science, Data Science or equivalent.