Data and AI Engineering Manager

Capital Group•Irvine, CA

5d•$234,276 - $398,269•Hybrid

About The Position

As an Engineering Manager in the Enterprise Data Office, you'll lead the engineering team behind some of the most consequential data and AI work at Capital Group. Your team builds the governed data products that CGMC, the Capital Operating Group, the Executive Office, and Business Councils rely on to run the business. We're looking for a hands-on leader who sets the technical direction and builds alongside the team, with the curiosity to explore what AI can do for data and the judgment to know where it shouldn't. You’ll build the founding engineering team for CBI: hiring, coaching, and setting the operating model for how AI agents ship alongside them. This role is based in Southern California, with time across our Los Angeles and Irvine offices.

Requirements

10+ years building full-stack data products end-to-end, hands-on across a modern lakehouse stack (Databricks, Unity Catalog, dbt, Python, SQL) with recent work in a modern UI framework such as React (or equivalent); Databricks Apps experience a plus
Direct experience embedding LLMs, retrieval-augmented generation, and agents into data engineering workflows, for example AI-assisted data quality, anomaly detection, or lineage tracing
Track record as a hands-on technical leader or engineering manager for a high-performing team. You develop, mentor, and coach a talented, diverse team, and you create an inclusive, engaging environment where every engineer can thrive
Track record operating production data platforms end-to-end - SLAs, data quality and contract enforcement, observability, cost, incident response, and CI/CD for data products serving demanding consumers
Strong grasp of data modeling and data product design from source ingest to consumer-fit products, with shared metrics and dimensions that BI tools and agents can consume; familiarity with Databricks Genie, Unity Catalog metric views, or Open Data Contract Standard (ODCS) a plus
Experience in Financial Services (ideally Investment Management), or a demonstrated ability to ramp quickly on complex regulated domains, with comfort partnering across product, business, risk, and platform stakeholders
You bring high agency, curiosity, and resilience to ambiguous problems, exercise disciplined judgment on trade-offs between scope, speed, and quality, and communicate clearly across technical and business audiences
Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience

Nice To Haves

Databricks Apps experience a plus
familiarity with Databricks Genie, Unity Catalog metric views, or Open Data Contract Standard (ODCS) a plus

Responsibilities

Hands-On AI-Native Data Product Delivery (40%): Your team ships the data and intelligence Capital Group runs on: everywhere it's consumed, by humans and AI. Consumer demand is outpacing anyone still writing pipelines by hand, so you'll extend EDO AI Catalyst (CG's AI sidekick for data product creators) with the skills, agents, and automation to ship at the pace the business needs. Your engineers review, steer, and own the hardest technical calls.
Engineering Vision, Solution Design, and AI for Data (30%): Set the solution design for CBI's data products (jointly with Product Leads) and how AI powers them: a modern data catalog for humans and agents, source-to-target lineage with impact analysis, and shared metrics and dimensions for the next generation of consumption (BI, agents, executive narratives). Own the hard calls on where AI belongs in the stack and where it doesn't.
Team Leadership and Growth (20%): Build the team, culture, and hiring bar from the ground up. Recruit, coach, and grow a diverse team of engineers, and set the operating model for how the team builds, ships, and maintains AI skills and agents alongside the humans.
Partnership and Enterprise Engagement (10%): Represent CBI's engineering voice in Enterprise Data Office forums (architecture, platform, AI governance, catalog federation), engage with business stakeholders as data consumers, and coordinate with Data Platform and Global Data Architecture as peer teams on the shared lakehouse foundation.