• Lead the org-wide data platform that powers every product and AI workflow across the company — from our award-winning AI-powered Global HR & Compliance Product to the Agentic AI Platform. • Build, mentor, and grow high-performing distributed engineering teams that thrive in a culture of autonomy, ownership, and operational excellence. • Lead design, development, and evolution of all core data platform services: streaming/event ingestion, lakehouse, and Knowledge Platform. • Ensure operational health, reliability, performance, and cost efficiency of the platform. • Own platform uptime, monitoring, and incident response, ensuring teams can rely on the platform to deliver critical products and AI features. • Collaborate with product-aligned teams to ensure platform adoption, integration, and best practices. • Shape technical strategy for distributed systems, streaming infrastructure, lakehouse, and Knowledge Platform. • Drive adoption of modern engineering practices: CI/CD, automated testing, documentation, and design reviews.

• Design, develop, and maintain data transformation pipelines using dbt. • Build reusable and scalable logic using Jinja and dbt macros. • Work extensively with dbt configurations, project structure, and parameterization. • Implement and optimize dbt materializations (incremental, snapshot, table, view, ephemeral). • Define and maintain data models aligned with business requirements. • Apply data quality checks and testing strategies using dbt and relevant packages. • Collaborate with cross-functional teams to translate business needs into technical solutions. • Troubleshoot and resolve data transformation and pipeline issues. • Contribute to best practices in data modeling and dbt development.

• Design, develop, and maintain data transformation pipelines using dbt. • Build reusable and scalable logic using Jinja and dbt macros. • Work extensively with dbt configurations, project structure, and parameterization. • Implement and optimize dbt materializations (incremental, snapshot, table, view, ephemeral). • Define and maintain data models aligned with business requirements. • Apply data quality checks and testing strategies using dbt and relevant packages. • Collaborate with cross-functional teams to translate business needs into technical solutions. • Troubleshoot and resolve data transformation and pipeline issues. • Contribute to best practices in data modeling and dbt development.

• Design, build, and maintain scalable data pipelines and ETL processes • Develop and optimize data architectures and data models • Work with large datasets across structured and unstructured sources • Ensure data quality, integrity, and performance optimization • Collaborate with Data Scientists, Analysts, and cross-functional teams • Implement data security and governance standards • Monitor and troubleshoot production data issues • Participate in code reviews and best practices implementation