• Build and maintain ETL/ELT data pipelines in Databricks and Spark, ensuring data is ingested, transformed, and delivered reliably for analytics and AI use cases. • Develop and evolve logical and physical data models to support reporting, experimentation, and advanced workflows (e.g., scoring models, signal generation). • Implement monitoring, alerts, and testing for data quality, timeliness, and lineage to ensure trustworthy data delivery. • Support workflow orchestration with Databricks Jobs, DBT, or equivalent scheduling tools to operate at scale. • Contribute to data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, or embedding workflows. • Design and optimize bulk GenAI data pipelines in Databricks to support generative AI applications at scale. • Partner with AI engineers and data scientists to enable experimentation, model training, and production-grade deployments. • Develop frameworks for data ingestion, transformation, governance, and monitoring across CRM, sales, and revenue systems. • Work with RevOps, sales, and customer success stakeholders to translate business needs into data requirements and stable technical implementations.