Manager, Data and AI Engineering

About the job

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, 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.
• 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.
• Handle the full lifecycle of data engineering projects — from discovery and planning through execution and production rollout.

Requirements

• Master's or Bachelor's degree in Computer Science or Information Systems, or equivalent experience.
• 10+ overall years in Data Engineering, Software Engineering, or web application development.
• At least 3+ years specifically in a leadership or engineering management role.
• Willingness to actively write code, prototype, and build production systems.
• Intimate knowledge of the AWS ecosystem, including Amazon S3, EC2, IAM, Lambda, and API Gateway.
• Proven experience operationalizing Large Language Models (LLMs) into autonomous agents that can plan, use tools, and implement multi-step workflows.
• Proven deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows.
• Hands-on experience scaling Unity Catalog is highly preferred.
• Active Databricks Certifications (e.g., Data Engineer Professional, Generative AI Engineer Associate) are a plus.
• Active AWS Certifications (e.g., Certified Data Engineer – Associate or Solutions Architect – Professional) are a plus.
• Background in managing multi-functional teams that blend data engineers with front-end and back-end software developers is a plus.
• Knowledge of supply chain business processes for Plan, Make, Deliver & Services is a plus.

Benefits

• Equity
• Benefits