Data Studio Lead

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.