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) 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)