Manager, Data and AI Engineering

Responsibilities

• Design and build scalable data and AI platforms using Databricks, AWS, and modern cloud-native engineering patterns.
• Deliver robust ETL/ELT, streaming, and CDC pipelines using technologies such as Spark, Kafka, Delta Lake, and AWS-native services.
• Enable delivery of AI-powered use cases including RAG applications, AI agents, tool-calling workflows, and data-driven web apps.
• Design data models using Star Schema, Snowflake Schema, and Data Vault patterns appropriate to the use case — optimizing for analytical query performance, data governance, and extensibility.
• Implement data quality frameworks, observability, alerting, and monitoring to ensure pipeline integrity and production reliability.
• Build the data foundation for GenAI, agentic AI, and advanced analytics initiatives, including RAG pipelines, vector search, knowledge graphs, and multi-agent orchestration patterns
• Partner with product, business, analytics, and AI collaborators to translate requirements into secure, scalable, and production-ready solutions.
• Oversee resource planning, prioritization, project execution, and delivery across multiple concurrent initiatives, and mentor engineers, grow technical capability across the team, and develop a culture of accountability, innovation, and continuous improvement.
• Provide hands-on technical leadership across architecture, design reviews, implementation guidance, and production readiness, and handle the full lifecycle of data engineering projects — from discovery and planning through execution and production rollout.
• What we need to see

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