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)