EXL is a leading global analytics and digital solutions company that partners with clients to improve business outcomes and accelerate growth, leveraging advanced analytics, AI, digital transformation, and deep domain knowledge across multiple industries
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
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.
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).
Don't want to miss the next one?
Subscribe to daily email alerts for roles matching your interests.
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).
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.
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.
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).
