Lead agentic delivery and large-scale data migration/modernisation programs, institutionalise DataOps and CI/CD, design multi-agent workflows for SDLC acceleration, ensure security and responsible AI controls, and manage large cross-functional agile teams and client engagements.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
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