CoE Lead - Data Analytics

Job Description (JD) - Data & Analytics Center of Excellence (CoE) Lead

Role Overview

The Data & Analytics CoE Lead will establish, scale, and govern the Center of Excellence across data strategy, architecture, engineering, BI/visualization, advanced analytics, and AI/ML enablement. The role partners closely with Sales, Pre-Sales, Delivery, and Customer stakeholders for requirement discovery, solution design, delivery, post-implementation support, and measurable business outcomes. The Lead reports to Business Head and is accountable for capability building, pipeline growth, execution excellence, ROI and Customer relationship.

CoE Scope & Technology Landscape

• Data Platforms: Azure (Synapse/Fabric), AWS (Redshift/Glue), GCP (BigQuery/Dataproc); modern DWHs (Snowflake, Databricks).
• Data Engineering: ETL/ELT, streaming (Kafka/Kinesis), orchestration (Airflow/Azure Data Factory), Lakehouse patterns.
• Data Governance: Catalog/lineage (Purview/Collibra), stewardship, data quality frameworks, privacy/security controls.
• BI & Reporting: Power BI, Tableau, Looker; semantic models, metrics layers, self-service enablement.
• Advanced Analytics & AI/ML: ML lifecycle (feature stores, MLOps, monitoring/drift, retraining), NLP, forecasting, optimization.
• FinOps & Cost Management: workload sizing, tiering, and optimization for cloud data platforms.

Key Responsibilities

Strategic Leadership:

• A CoE Lead isn’t just managing teams; they’re building frameworks, governance models, and best practices from the ground up.”
• Prior leadership in setting up Data & Analytics CoEs is highly desirable.
• Driving GTM Strategy

CoE Strategy & Governance

• Define CoE charter, operating model, design authority, and governance cadences; set standards and guardrails.
• Publish reference architectures, data product templates, accelerators, and best practices.
• Institutionalize policies for data quality, privacy (PII), security, and compliance (e.g., ISO/ SOC/ GDPR-aligned).

Sales & Pre-Sales Enablement

• Co-create value propositions, GTM collateral, and solution blueprints with Sales/Pre-Sales.
• Lead/Support RFPs/RFIs, client demos, and POCs; craft ROI/TCO models and business cases.
• Develop case studies with quantified outcomes (cycle-time, adoption, DQ improvement, cost savings).

Customer Requirement Gathering & Solutioning

• Run discovery workshops to capture business objectives, data sources, KPIs, and constraints.
• Design end-to-end architectures spanning ingestion, storage, modeling, governance, BI, and ML.
• Define non-functional requirements (security, performance, reliability) and change management.

Delivery & Operational Excellence

• Oversee project delivery; enforce DataOps/MLOps, CI/CD, and quality gates; track SLAs and KPIs.
• Implement monitoring/observability for pipelines and models; establish SRE-like reliability for data platforms.
• Drive continuous improvement across performance, cost (FinOps), and user experience/adoption.

Leadership Reporting & Stakeholder Management

• Publish monthly/quarterly dashboards: pipeline, bookings, revenue/margin, delivery KPIs, DQ, adoption, and risk.
• Run governance forums with BU leaders and exec sponsors; manage dependencies and escalations.
• Own CoE financial plan; track investments, capability utilization, and ROI vs targets.

Required Skills & Experience

• 18+ years in Data & Analytics with 8-6 years in leadership roles building/running data platforms.
• Hands-on across data architecture, engineering (ETL/ELT/streaming), BI, and AI/ML; strong solution architecture skills.
• Experience with cloud platforms (Azure/AWS/GCP) and modern DWHs (Snowflake/Databricks); governance tools (Purview/Collibra).
• Proven pre-sales engagement: discovery, solutioning, POCs, estimation/pricing, ROI/TCO modelling.
• Operational leadership: DataOps/MLOps, reliability, and security/compliance in production.
• Excellent communication and stakeholder management; translate technical outcomes into business value.

Education & Certifications

• Bachelor's/Master's in Computer Science, Data Science, Information Systems, or related discipline.
• Preferred: Cloud (Azure/AWS/GCP), Snowflake/Databricks, Power BI/Tableau, and ML/MLOps certifications.