Agentic Data Delivery Lead

Roles & Responsibilities

1. Delivery Leadership & Strategy

• Lead end-to-end delivery of large-scale data engineering and modernisation programs (Data Lakes, Data Warehousing, Lakehouse, Data Migration).
• Define and drive Agentic AI-led delivery models to improve productivity across SDLC.
• Own delivery governance, quality, timelines, and client satisfaction across multiple accounts.

2. Data Platform & Modernisation Leadership

• Drive enterprise-level data transformations including:
  • On-prem Cloud migrations
  • Cloud Cloud transformations
  • Legacy DW Modern Lakehouse / Warehouse
  • Platform modernisation & digitalisation initiatives
• Architect scalable, resilient, and future-ready data ecosystems.

3. GenAI / Agentic AI Delivery

• Lead design and implementation of Agentic AI / LLM-based solutions in enterprise data ecosystems.
• Define delivery patterns for multi-agent systems, RAG pipelines, automation, and intelligent workflows.
• Drive adoption of AI-led accelerators across delivery programs.

4. Solutioning & Pre-Sales

• Lead RFP / RFI / proactive solutioning for large deals.
• Build value-led proposals including solution architecture, costing, and delivery models.
• Work closely with sales and account leadership in deal shaping.

5. CoE & Capability Building

• Build, scale, and run Data / AI / Agentic AI Centres of Excellence (CoEs).
• Define frameworks, accelerators, reusable assets, and best practices.
• Develop internal capability maturity models and delivery standards.

6. Data Governance:

• Define and enforce enterprise-wide data governance frameworks covering data quality, lineage, metadata, and access controls
• Ensure compliance with regulatory requirements, data privacy (PII), and security standards across all data and AI platforms
• Embed governance controls within data engineering pipelines and Agentic AI / GenAI delivery workflows
• Establish standards for data lifecycle management, audit readiness, and risk mitigation
• Implement AI governance practices, including model oversight, ethical AI usage, and guardrails
• Collaborate with stakeholders to drive adoption of governance policies across global delivery teams
• Engage with senior client stakeholders (CXO / VP level).
• Act as a trusted advisor on data strategy, AI adoption, and digital transformation.
• Manage multi-geography teams and global client engagements.

7. Stakeholder & Client Management

8. Partnerships & Ecosystem

• Drive strategic partnerships with hyperscalers and technology partners such as:
  • AWS, Azure, GCP
  • Snowflake, Databricks
  • OpenAI, Anthropic and GenAI ecosystem providers
• Influence joint GTM strategies and co-innovation initiatives.

9. Leadership & People Development

• Lead and mentor large cross-functional teams (delivery, architecture, engineering).
• Build leadership pipelines and strong engineering culture.
• Drive performance, engagement, and capability development.

Must Have Skills & Experience

• 20+ years of IT experience, with strong early career foundation in solution development / engineering.
• 10+ years of experience in data engineering & platform delivery, including:
  • Data Lake / Data Warehouse implementation
  • Data migration (On-prem to Cloud / Cloud to Cloud)
  • Platform modernisation & digital transformation
• 3-4 years of hands-on experience in GenAI / Agentic AI solutions.
• Proven experience in building and leading large delivery teams and CoEs.
• Strong experience in stakeholder management and global client engagement.
• Demonstrated experience in RFPs, RFIs, and large deal solutioning.

Technology Exposure (Mandatory)

• Programming: Python
• Data Engineering: ETL/ELT, Big Data frameworks (Spark, Hadoop ecosystem)
• Data Platforms: Snowflake, Databricks, Lakehouse architectures
• Cloud: AWS / Azure / GCP
• AI/GenAI: LLMs, RAG, Agentic frameworks, orchestration tools

Good to Have Skills

• Experience in multi-agent architectures and AI-driven automation of SDLC
• Exposure to MLOps, DataOps, and AI governance frameworks
• Experience in industry domains such as Insurance, Banking, Healthcare, Retail
• Thought leadership (whitepapers, POVs, client presentations)

Roles & Responsibilities

1. Delivery Leadership & Strategy

• Lead end-to-end delivery of large-scale data engineering and modernisation programs (Data Lakes, Data Warehousing, Lakehouse, Data Migration).
• Define and drive Agentic AI-led delivery models to improve productivity across SDLC.
• Own delivery governance, quality, timelines, and client satisfaction across multiple accounts.

2. Data Platform & Modernisation Leadership

• Drive enterprise-level data transformations including:
  • On-prem Cloud migrations
  • Cloud Cloud transformations
  • Legacy DW Modern Lakehouse / Warehouse
  • Platform modernisation & digitalisation initiatives
• Architect scalable, resilient, and future-ready data ecosystems.

3. GenAI / Agentic AI Delivery

• Lead design and implementation of Agentic AI / LLM-based solutions in enterprise data ecosystems.
• Define delivery patterns for multi-agent systems, RAG pipelines, automation, and intelligent workflows.
• Drive adoption of AI-led accelerators across delivery programs.

4. Solutioning & Pre-Sales

• Lead RFP / RFI / proactive solutioning for large deals.
• Build value-led proposals including solution architecture, costing, and delivery models.
• Work closely with sales and account leadership in deal shaping.

5. CoE & Capability Building

• Build, scale, and run Data / AI / Agentic AI Centres of Excellence (CoEs).
• Define frameworks, accelerators, reusable assets, and best practices.
• Develop internal capability maturity models and delivery standards.

6. Data Governance:

• Define and enforce enterprise-wide data governance frameworks covering data quality, lineage, metadata, and access controls
• Ensure compliance with regulatory requirements, data privacy (PII), and security standards across all data and AI platforms
• Embed governance controls within data engineering pipelines and Agentic AI / GenAI delivery workflows
• Establish standards for data lifecycle management, audit readiness, and risk mitigation
• Implement AI governance practices, including model oversight, ethical AI usage, and guardrails
• Collaborate with stakeholders to drive adoption of governance policies across global delivery teams
• Engage with senior client stakeholders (CXO / VP level).
• Act as a trusted advisor on data strategy, AI adoption, and digital transformation.
• Manage multi-geography teams and global client engagements.

7. Stakeholder & Client Management

8. Partnerships & Ecosystem

• Drive strategic partnerships with hyperscalers and technology partners such as:
  • AWS, Azure, GCP
  • Snowflake, Databricks
  • OpenAI, Anthropic and GenAI ecosystem providers
• Influence joint GTM strategies and co-innovation initiatives.

9. Leadership & People Development

• Lead and mentor large cross-functional teams (delivery, architecture, engineering).
• Build leadership pipelines and strong engineering culture.
• Drive performance, engagement, and capability development.

Must Have Skills & Experience

• 20+ years of IT experience, with strong early career foundation in solution development / engineering.
• 10+ years of experience in data engineering & platform delivery, including:
  • Data Lake / Data Warehouse implementation
  • Data migration (On-prem to Cloud / Cloud to Cloud)
  • Platform modernisation & digital transformation
• 3-4 years of hands-on experience in GenAI / Agentic AI solutions.
• Proven experience in building and leading large delivery teams and CoEs.
• Strong experience in stakeholder management and global client engagement.
• Demonstrated experience in RFPs, RFIs, and large deal solutioning.

Technology Exposure (Mandatory)

• Programming: Python
• Data Engineering: ETL/ELT, Big Data frameworks (Spark, Hadoop ecosystem)
• Data Platforms: Snowflake, Databricks, Lakehouse architectures
• Cloud: AWS / Azure / GCP
• AI/GenAI: LLMs, RAG, Agentic frameworks, orchestration tools

Good to Have Skills

• Experience in multi-agent architectures and AI-driven automation of SDLC
• Exposure to MLOps, DataOps, and AI governance frameworks
• Experience in industry domains such as Insurance, Banking, Healthcare, Retail
• Thought leadership (whitepapers, POVs, client presentations)