- *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.
- 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
- 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.
- 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
- 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)
- *Qualifications: 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.
- 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
- 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)