Senior Data Platform Engineer

<h2><strong>About the Team:</strong></h2> <p>Rubrik is building the world’s first cloud data management platform for data protection, search, analytics, archival and copy data management for hybrid cloud enterprises. Recover, manage, and secure your data on any platform which makes it very easy for all businesses to manage their data. Our goal is to democratize public cloud for every enterprise and provide them with the architecture to manage data at scale. With our massive undertaking, there is a lot of opportunity for personal growth and contribution to a product unlike anything built before. </p> <p>We are today the Fastest growing Enterprise Startup ever in the history of Silicon Valley!</p> <h2><span style="font-family: arial, helvetica, sans-serif;"><strong>About the role:</strong></span></h2> <p>The <strong>Data Platform Engineer</strong> is responsible for designing and enforcing data platform architecture and modeling standards, enabling analytics, conversational analytics, and AI-driven use cases across the enterprise. The role supports platform level POCs, including data ingestion frameworks, and leverage AI capabilities to process unstructured structured data. The Data Platform Engineer defines and governs data modeling and data management standards, enforces enterprise data governance frameworks, and partners closely with architect, data engineers, and analysts teams to support both traditional BI and emerging AI/GenAI workloads.</p> <h2><strong>What you'll do:</strong></h2> <h3><strong>Data Modeling Analytics Enablement</strong></h3> <ul> <li>Develop and maintain logical, and physical data models to support enterprise analytics and AI/Gen AI use cases.</li> <li>Design data models that enable:</li> <ul> <li>BI and reporting</li> <li>Forecasting and predictive models</li> <li>Feature engineering for ML models</li> <li>GenAI workloads</li> </ul> <li>Collaborate with data engineering teams to implement data transformations, pipelines and orchestration using <strong>dbt, SQL </strong>and integrate them into DevOps workflows and CI/CD pipelines</li> <li>Enable and optimize analytics platforms such as Snowflake (or equivalent) and Tableau (or equivalent) for both traditional and AI-driven workloads</li> <li>Ensure data models and transformations adhere to enterprise governance, standards, and best practices, supporting scalable and reliable analytics.</li> </ul> <h3><strong>Data Integration Processing</strong></h3> <ul> <li>Define ingestion and transformation patterns for batch, streaming, and near real-time data.</li> <li>Implement ETL/ELT processes using tools such as FiveTran, dbt, SQL, and Snowflake pipelines to ensure high quality, reliable, and scalable data flows.</li> <li>Enable real time and batch processing capabilities to support analytics, AI, and GenAI workloads.</li> <li>Collaborate with data engineers, platform architects, and analytics teams to standardize ingestion patterns, maintain metadata, and ensure governance compliance</li> <li>Optimize pipelines for performance, cost efficiency, and scalability, including monitoring, alerting, and automated recovery mechanisms.</li> </ul> <h3><strong>Platform POCs AI-Driven Data Enablement</strong></h3> <ul> <li> <ul> <li>Lead POCs on the data platform to evaluate and demonstrate emerging capabilities for analytics, AI, and GenAI workloads.</li> <li>Implement unstructured-to-structured data processing pipelines using AI/ML techniques to extract, transform, and enrich data for downstream consumption.</li> <li>Design and execute data scoring, feature generation, and enrichment processes to support predictive analytics, ML models, and GenAI applications.</li> <li>Leverage Snowflake Cortex and other platform tools to prototype scalable, high performance data workflows.</li> <li>Manage and maintain the data platform, including schema evolution, performance optimization, and platform monitoring</li> <li>Collaborate with platform, data engineering, and analytics teams ... (truncated, view full listing at source)