Practice Lead

Data & AI Practice Lead

Role Summary

The Data & AI Practice Lead drives growth and delivery excellence across data platform modernization, analytics, AI/GenAI, and intelligent decisioningbuilt on the Microsoft cloud ecosystem. You will shape the regional strategy, develop differentiated offerings, lead complex client engagements, and build high-performing teams to deliver measurable outcomes (insights, productivity, cost optimization, and responsible AI).

Key Responsibilities

Practice & Portfolio Leadership

Define and execute the Data & AI practice strategy aligned to Microsofts Data & AI stack and priorities.

Own portfolio across Data Strategy, Modern Data Platforms, Analytics & BI, AI/ML, GenAI, Data Governance, and Data Operations.

Build repeatable industry accelerators, reference architectures, reusable pipelines, and solution playbooks.

Establish governance for delivery quality, security, responsible AI, and scalable engineering standards.

Client Advisory & Architecture Leadership

Act as senior advisor to CIO/CDO/CTO on:

• Data platform modernization and cloud data estate strategy
• Enterprise analytics and semantic modeling
• AI/ML operating models and MLOps
• GenAI adoption (use-case prioritization, risk controls, scaling patterns)

Lead architecture/design reviews for large programs including data residency, privacy, and regulatory constraints.

Define target operating models for DataOps/MLOps, data product teams, and platform governance.

Growth, Sales & Microsoft Partnership

Own practice pipeline: originate opportunities, shape pursuits, and close deals across industries.

Lead pre-sales: vision workshops, assessments, proposals, estimates, SoWs, and exec storytelling.

Drive strong relationships with Microsoft (account teams, engineering, ISVs) to co-sell and co-innovate.

Build client-ready business cases: value realization, TCO, adoption roadmaps, and measurable KPIs.

Program Delivery & Oversight

Provide executive oversight to key accounts: ensure delivery against scope, schedule, budget, and outcomes.

Lead programs such as:

• Cloud data estate build/modernization
• Data migration and platform consolidation
• Enterprise BI modernization
• AI/ML and GenAI solution delivery and scaling

Set and track metrics: data quality, platform reliability, cost, time-to-insight, model performance, adoption.

People Leadership & Capability Building

Build, mentor, and scale multi-disciplinary teams (data engineers, architects, analysts, DS/ML engineers, AI architects).

Create skills frameworks and certification targets; lead hiring and partner strategy.

Champion communities of practice, reusable IP, and knowledge-sharing culture.

Microsoft-Centric Technical Scope (Expected strength)

You may not code daily, but you must be able to lead architecture decisions, challenge teams, and credibly engage senior stakeholders.

Data Platform & Engineering (Microsoft Fabric + Azure)

Microsoft Fabric: Lakehouse/Warehouse, Data Factory, OneLake, semantic models, Real-Time Analytics (as relevant)

Azure data services where applicable: ADLS, Azure SQL, Synapse (legacy), Databricks (if hybrid), Event Hubs, Functions

Ingestion patterns: batch/streaming/CDC; medallion architectures; ELT/ETL strategies

Performance engineering, data modeling, partitioning, cost optimization

Analytics & BI

Power BI enterprise deployment: governance, semantic modeling, row-level security, performance tuning

Data product mindset and KPI design; enterprise reporting modernization

AI / ML / MLOps

Azure Machine Learning, ML pipelines, feature engineering patterns, model governance, monitoring and drift detection

CI/CD for ML, model lifecycle, experimentation-to-production practices

Responsible AI practices and controls

GenAI (Azure OpenAIcentric)

Use-case discovery and prioritization; value and risk assessment

RAG patterns, grounding, embeddings, vector stores, prompt engineering standards

Evaluation frameworks, safety filters, data leakage prevention, and operationalization patterns

Integration patterns with enterprise apps, APIs, and identity (Entra ID)

Governance, Security & Compliance

Data governance: cataloging, lineage, access controls, privacy, retention

Microsoft Purview (preferred), sensitivity labels, DLP concepts

Zero Trust alignment: Entra ID, RBAC, PIM, key management

Regulated industry delivery experience (MAS TRM / BNM RMiT / PDPA etc. as applicable)

Qualifications

Bachelors degree in Computer Science/Engineering (or equivalent experience).

1520+ years in data/analytics/AI leadership, consulting, or engineering with strong Microsoft ecosystem depth.

Proven leadership of large transformation programs and multi-million-dollar pursuits.

Strong executive communication and stakeholder management.

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