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)
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)
