VP - Data Studio Lead – Agentic Accelerated Delivery

Data Studio Lead – Candidate Profile Summary

We are looking for a highly experienced technology leader with 20+ years of experience in enterprise data engineering, data platform modernization, and large-scale solution delivery. The ideal candidate should come from Tier 1 technology or consulting organizations where AI adoption, digital transformation, and enterprise solution delivery are mature and well established.

The candidate must have a strong hands-on technical background in software development (Java and/or Python) or data engineering . Candidates with only BI, reporting, analytics, or platform administration backgrounds will not be a fit. This is a leadership role that requires deep technical credibility, and not just people management experience.

Strong expertise in modern data engineering technologies is essential, including Databricks, Snowflake, Python , and cloud-based data platforms. The candidate should have successfully led enterprise data migration and modernization programs , working closely with implementation and migration partners across large transformation initiatives.

The role also requires experience in pre-sales and solutioning activities , including responding to RFI, RFP, and RFQ processes, developing solution proposals, estimating delivery effort, and engaging with clients on technical and business discussions.

On the AI front, the ideal candidate should have hands-on experience designing, developing, or delivering Generative AI and Agentic AI solutions . They should understand how AI can accelerate software and data engineering lifecycles, be familiar with the latest AI tools and frameworks, and have experience building and leading AI-focused engineering teams.

Additionally, the candidate should possess a strong understanding of AI governance , including responsible AI practices, security, compliance, guardrails, and enterprise adoption frameworks. Keeping pace with emerging AI technologies and identifying opportunities to leverage them for business value is critical.

This role is expected to lead the Data Studio , define the technology roadmap, drive innovation, establish best practices, build high-performing engineering teams, and spearhead new strategic initiatives in enterprise data engineering and AI-driven delivery.

About the Role:
We are seeking a Data Studio Lead to drive agentic accelerated delivery using LLMs and multi-agent
approaches (e.g., Claude, OpenAI and similar). The role’s primary focus is enabling data migration,
modernisation, and DataOps powered by agentic capabilities to accelerate the SDLC (analysis → build → test
→ release → operate). This leader will manage large agile teams, deliver outcomes for clients across
industries, and establish repeatable accelerators, patterns, and governance for safe, high-quality GenAI
adoption.

 

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 w ithout
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.
 

Education
• Bachelor’s/Master’s in Computer Science, Information Systems, Data Engineering, or related
discipline (or equivalent experience).