Agentic Data Delivery Lead

Job description

**Job Description: 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) **Responsibilities: 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