• Drive the end-to-end implementation of Microsoft Fabric across the organization • Lead the setup of ETL/ELT pipelines • Design and implement the bronze, silver, and gold layers of our data Lakehouse • Oversee the configuration of key Fabric components including Data Factory, One Lake, and Power BI • Explore advanced capabilities such as Microsoft Copilot to accelerate analytics and automation • Collaborate with the D&A team and business stakeholders to translate business needs into scalable data architecture • Define and implement a modern data Lakehouse framework using Microsoft Fabric • Engage with executives and functional leaders to establish metrics, strategies, and implement roadmaps for enterprise-wide analytics

• Design and implement GenAI-based solutions that optimize critical stages of the DDLC (Data Development Life Cycle). • Develop agents that act as assistants in creating Databricks notebooks, pipelines in ADF, and structures in Microsoft Fabric. • Create workflows that integrate LLMs into our ecosystem (SQL Server and Power BI), automating tasks such as data documentation and creation of DAX measures. • Apply advanced prompt engineering techniques to ensure the AI generates optimized, secure, and high-performance PySpark and T-SQL code. • Define quality metrics to validate AI-generated artifacts (e.g., ensure efficiency of generated queries). • Ensure solutions align with Data Governance, security, and privacy practices. • Stay up to date on AI capabilities within Microsoft Fabric and Databricks to maximize the use of native tools versus custom developments.

• Own the tenant modeling strategy, governing organization structure, resource isolation (compute/IAM), and automated provisioning. • Establish CI/CD standards, Cloud Run deployment automation, and release governance to ensure environment consistency. • Design enterprise-wide logging, tracing, and SLO-driven alerting standards to ensure production systems are diagnosable at scale. • Build multi-tenant data engineering frameworks using BigQuery, Cloud Composer/Airflow, and dbt (Medallion architecture). • Define cloud patterns for high-throughput Pub/Sub architectures and secure data sharing across shared platform services. • Prevent platform fragmentation by establishing global standards for onboarding, security, and cost optimization. • Direct a distributed team (including India-based GCC) to deliver architectural reviews and operational readiness across time zones.

• Design, build, and maintain scalable data pipelines (ETL/ELT) and data models • Extract, transform, and load data from ERP, CRM, field service systems, and blob or database storage • Provide clean, well-structured, and documented datasets, including analytics-ready dbt models • Partner with analytics and business teams to ensure data is accessible, understandable, and fit for reporting and analysis • Assist with data integration for acquisitions • Support the data foundation required to track value creation initiatives and operational improvements • Implement data quality rules and operate within data governed environments.