• Architect, build, deploy, and operate scalable ETL/ELT data pipelines supporting analytics, ML, and GenAI workloads.
• Use AI/ML and GenAI to design and build data pipelines, including: AI‑assisted pipeline and schema design; AI‑generated and optimized transformation logic and code; automated test generation, data validation, and performance tuning; AI‑driven documentation and operational runbooks.
• Apply AI/ML techniques to automate data engineering workflows, including ingestion, schema evolution, data quality checks, anomaly detection, and pipeline optimization.
• Build and maintain feature engineering pipelines, training datasets, and feedback loops for production ML and GenAI systems.
• Enable GenAI use cases, including unstructured data ingestion, retrieval pipelines (RAG), and governance‑aware AI data flows.
• Ensure enterprise standards for reliability, observability, lineage, security, and compliance across all data and AI pipelines.
• Partner with product, program, analytics, and AI teams in Agile delivery models to translate business needs into scalable AI‑enabled data solutions.
• Provide senior technical leadership through architecture reviews, mentoring, and setting data & AI engineering standards.