Total eBiz Solutions
Website:
totalebizsolutions.com
Job details:
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 decisioning—built 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 Microsoft’s 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 OpenAI–centric)
• 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
• Bachelor’s degree in Computer Science/Engineering (or equivalent experience).
• 15–20+ 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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