• 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.

• Define, evolve and ensure adoption of data architectures on Azure, aligned with business strategy and industry best practices • Act as a technical reference on complex initiatives, supporting architectural and technology decisions • Lead implementation of large-scale data solutions using services such as Azure Data Factory, Azure Databricks and Azure Synapse • Establish standards, frameworks and data engineering best practices, ensuring quality, governance and security • Design, build and optimize data pipelines with a focus on performance, scalability and operational efficiency • Drive modernization and migration initiatives for data platforms to the cloud • Collaborate with business, architecture, security and engineering teams to ensure integrated and sustainable solutions • Serve as a technical mentor, supporting squads and sharing knowledge across the team • Analyze and optimize costs, performance and cloud resource utilization, driving continuous improvements