Head - Data Engineering & AI

Role - Head - Data Engineering & AI

Head - Data Engineering & AI

Key Responsibilities:

• Leadership & Strategy

Define and own the vision, strategy, and roadmap for enterprise data lake on Azure and BCP for data lake.

Lead and mentor high-performing data engineering teams across multiple domains.

Drive adoption of modern data architectures (data lake, data mesh, data fabric, or warehouse).

• Data Platform & Architecture

Architect and oversee scalable, reliable, and secure data pipelines and data platforms.

Ensure integration of multiple data sources (structured, semi-structured, and unstructured) into unified, governed data assets.

Lead migration and modernization initiatives (e.g., on-prem to cloud, big data platforms).

• Data Management, Governance, Compliance

Ensure adherence to data quality, metadata management, and master data management practices.

Partner with governance teams to implement policies for data security, Regulatory compliance (RBI etc.), and other compliance requirements.

Establish monitoring and alerting frameworks for proactive issue detection.

Manage enterprise data dictionary

• Collaboration & Enablement

Collaborate with product, business teams, and other teams and Support the organization’s AI/ML and advanced analytics initiatives through robust data foundations

• Performance & Vendor Management

Define and track KPIs for data engineering effectiveness, cost optimization, and platform reliability.

Manage vendor relationships for data platforms, tools, and services, ensuring alignment with SLAs and business outcomes.

Preferred Qualifications:

Bachelor’s / master’s degree in engineering or equivalent

Experience: 15–17 years of progressive experience in data, analytics, and AI, with at least 5 years in a leadership role driving enterprise-level transformation

Hands on experience with technologies such as Cloud (AWS/Azure/OCI/GCP)

Data Tools like Data Bricks, Synapse, Kafka, Snowflake, Azure data Lake, ETL Tools, Talend, Databases etc.

Strong understanding of data governance, security and regulatory compliance framework