Job Description: Key Responsibilities (Roles & Responsibilities)
- Define and evolve the enterprise data architecture roadmap aligned to business goals and digital transformation programs.
- Own architecture standards for data platforms (lakehouse/warehouse), data integration, and data products including reference architectures, patterns, and reusable templates.
- Design end-to-end cloud data architectures across ADLS Gen2, Azure Synapse/Fabric Warehouse, Databricks/Spark, and streaming/real-time components as required.
- Establish and govern data modeling standards (conceptual/logical/physical), dimensional models (star/snowflake), semantic layer design, and performance-optimized data layers.
- Architect scalable ETL/ELT and orchestration frameworks using Azure Data Factory/Synapse Pipelines/Fabric Data Factory/Databricks with CI/CD, parameterization, and observability.
- Implement data governance and lineage and define metadata standards and stewardship workflows.
- Define data quality strategy: profiling, rules, controls, monitoring dashboards, and issue remediation process with measurable SLAs.
- Architect master data and reference data management patterns (canonical models, mapping, hierarchy management) and integration with source systems.
- Design and enforce security architecture: RBAC/ABAC patterns, data classification, encryption, key management, PII controls, and privacy-by-design.
- Drive platform reliability and cost efficiency: workload sizing, performance tuning, capacity planning, and FinOps-oriented optimizations.
- Partner with SI Partners to ensure data architecture supports high-performing Power BI/DWH semantic models, self-service analytics, and executive reporting.
- Involve in architecture reviews with internal teams and SI partners; mentor engineers/analysts and drive adoption of best practices.
- Engage with senior stakeholders (business, product, technology, risk/compliance) to translate requirements into architectural decisions and communicate risks/trade-offs.
Stay current on emerging capabilities in Microsoft Fabric, Synapse, Databricks Lakehouse, and AI-assisted analytics (e.g., Copilot/agentic patterns) and drive modernization initiatives.
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CXO & Stakeholder Engagement
- Build and maintain strong relationships with key stakeholders to align analytics strategy with business priorities.
- Act as a trusted advisor, translating complex data insights into actionable business recommendations.
- Ensure stakeholder buy-in for enterprise data initiatives and foster collaboration across functions
Innovation & Emerging Technology
- Stay ahead of emerging trends in data, BI, and GenAI , particularly in Microsoft Fabric, Databricks Lakehouse, CoPilot for BI, and Agentic AI architectures .
- Champion AI-augmented BI , predictive reporting, and conversational analytics to enhance insight discovery.
Drive continuous modernization through internal hackathons, accelerator programs, and cross-functional innovation initiatives.
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Why This Role Matters
The AVP – Data Analytics role is pivotal to building trusted, governed, and scalable enterprise data foundations that accelerate digital transformation, enable real-time and self-service insights, and prepare the organization for GenAI and AI/ML ready data products.
Responsibilities: Key Responsibilities (Roles & Responsibilities)
- Define and evolve the enterprise data architecture roadmap aligned to business goals and digital transformation programs.
- Own architecture standards for data platforms (lakehouse/warehouse), data integration, and data products including reference architectures, patterns, and reusable templates.
- Design end-to-end cloud data architectures across ADLS Gen2, Azure Synapse/Fabric Warehouse, Databricks/Spark, and streaming/real-time components as required.
- Establish and govern data modeling standards (conceptual/logical/physical), dimensional models (star/snowflake), semantic layer design, and performance-optimized data layers.
- Architect scalable ETL/ELT and orchestration frameworks using Azure Data Factory/Synapse Pipelines/Fabric Data Factory/Databricks with CI/CD, parameterization, and observability.
- Implement data governance and lineage and define metadata standards and stewardship workflows.
- Define data quality strategy: profiling, rules, controls, monitoring dashboards, and issue remediation process with measurable SLAs.
- Architect master data and reference data management patterns (canonical models, mapping, hierarchy management) and integration with source systems.
- Design and enforce security architecture: RBAC/ABAC patterns, data classification, encryption, key management, PII controls, and privacy-by-design.
- Drive platform reliability and cost efficiency: workload sizing, performance tuning, capacity planning, and FinOps-oriented optimizations.
- Partner with SI Partners to ensure data architecture supports high-performing Power BI/DWH semantic models, self-service analytics, and executive reporting.
- Involve in architecture reviews with internal teams and SI partners; mentor engineers/analysts and drive adoption of best practices.
- Engage with senior stakeholders (business, product, technology, risk/compliance) to translate requirements into architectural decisions and communicate risks/trade-offs.
Stay current on emerging capabilities in Microsoft Fabric, Synapse, Databricks Lakehouse, and AI-assisted analytics (e.g., Copilot/agentic patterns) and drive modernization initiatives.
-
CXO & Stakeholder Engagement
- Build and maintain strong relationships with key stakeholders to align analytics strategy with business priorities.
- Act as a trusted advisor, translating complex data insights into actionable business recommendations.
- Ensure stakeholder buy-in for enterprise data initiatives and foster collaboration across functions
Innovation & Emerging Technology
- Stay ahead of emerging trends in data, BI, and GenAI , particularly in Microsoft Fabric, Databricks Lakehouse, CoPilot for BI, and Agentic AI architectures .
- Champion AI-augmented BI , predictive reporting, and conversational analytics to enhance insight discovery.
- Drive continuous modernization through internal hackathons, accelerator programs, and cross-functional innovation initiatives.
