Data Engineer Lead

Capital Group•Los Angeles, CA

9d•$201,683 - $342,072•Hybrid

About The Position

As a Data Engineer Lead in Capital Solutions Group Technology (CSGT), you will set the technical direction for the data platform and data products that power portfolio construction, investment research, and monitoring capabilities for the Capital Solutions Group (CSG), the Capital Group investment unit that manages our multi-asset fund of funds. Our investment professionals rely on this data to construct and monitor portfolios, evaluate underlying funds, and make investment decisions. This is a senior, hands-on individual contributor role. You will write code, own architecture and delivery, and partner closely with investment group, product management, and technology leaders. You will be a leader in our AI-first engineering practices, where engineers direct AI agents through clear specifications, context, and verification. You will build the trusted data foundation that AI applications need to answer complex investment questions. You have an agile mindset and will lead the design, implementation, and delivery of large-scale, critical, and complex data architecture, storage, and pipelines. You are passionate about our mission, and committed to driving superior long-term investment results through the application of modern engineering and data management methods.

Requirements

10+ years of experience in data or software engineering, including technical leadership of complex production data platforms delivered across multiple teams.
Strong hands-on Python and SQL skills, sound software design judgment, and a deep understanding of distributed data processing, query performance, and automated testing.
Production experience with Databricks on AWS, including PySpark, Delta Lake, Unity Catalog, Databricks Jobs, Databricks SQL, and Databricks Asset Bundles, and you understand the security, access, and cost implications of your designs.
Orchestrated production pipelines with Apache Airflow (including Astronomer) and built tested transformations with dbt, with reliable retries, backfills, and dependency management.
Strong data modeling and governance experience, including dimensional and time-series models, slowly changing dimensions, bi-temporal history, data contracts, lineage, and semantic metadata.
Implemented data quality, observability, and CI/CD for data platforms using tools such as Deequ, dbt tests, Lakehouse Monitoring, Datadog, Terraform, and Harness.
Experience preparing governed data for AI through natural-language-to-SQL tools such as Databricks Genie, semantic metadata, or other governed data-access patterns.
Use AI coding agents well beyond code completion: writing specifications, supplying context, running tests, and reviewing generated changes through source control.
Evaluate AI-generated output with representative test cases, regression tests, execution traces, and human review, and you can distinguish a plausible answer from a verified one.
Understand prompt injection, sensitive data handling, and least-privilege access, and can design approval boundaries and audit trails for agents working against enterprise systems.
Lead architecture discussions, influence without formal authority, develop other engineers, and explain technical choices and trade-offs clearly to investment professionals and technology leaders.
An agent of change with a sense of urgency: you question how work gets done and remove or automate what does not add value, while respecting what came before.
A bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience with investment management data such as portfolios, positions, returns, exposures, benchmarks, and attribution, or with multi-asset portfolio construction.
Experience building LLM applications or agent workflows that call tools and APIs, including context management, retrieval, state, and error handling.
Familiarity with Model Context Protocol (MCP).
Experience with PostgreSQL, SQL Server, or Lakebase, or with modernizing legacy data platforms onto a Lakehouse.

Responsibilities

Set the data engineering strategy and roadmap for CSGT, including the Lakehouse architecture on Databricks and AWS. Make and explain decisions on scalability, security, reliability, and cost, and influence standards and practices across adjacent teams and the wider Capital Group technology organization, contributing to centers of excellence and firm-wide engineering practices.
Design and build ingestion, transformation, and serving pipelines with Databricks, PySpark, Delta Lake, dbt, and Airflow, and create reusable patterns and frameworks so the team delivers consistent, maintainable data products.
Analyze new structured and unstructured datasets at the business-capability level and fit them into the platform’s data domains and subject areas.
Own delivery of complex, cross-team data initiatives from requirements through production support, including estimates, work breakdown, sequencing, dependencies, and cost. Surface risks early and balance immediate business needs with a durable platform.
Lead the continued evolution of AI-first engineering: turn business outcomes into well-defined specifications, engineer the business, architectural, and repository context agents need, and direct agents to plan, build, test, and document changes in small, reviewable increments.
Build reusable agent workflows, skills, and tool integrations for data engineering work such as profiling, source-to-target mapping, pipeline and test generation, schema-change analysis, and incident investigation. Define which actions agents can take on their own, which require human approval, and how agent activity is reviewed and traced.
Make data understandable and reliable for AI through curated Unity Catalog metadata, lineage, business definitions, semantic models, and access controls, and connect it to Databricks Genie and other AI applications used by investment professionals. Define evaluation datasets and acceptance criteria for agents and AI-generated SQL and code.
Define the testing strategy across all layers of the platform, including performance, stability, and availability, and review and approve quality metrics before release. Build data quality, reconciliation, freshness, observability, and recovery controls into automated testing and CI/CD, with security and policy checks embedded from the start.
Partner with investment professionals and product managers to develop a shared product vision and ownership of business outcomes, demonstrating how data and AI can scale research and portfolio construction.
Raise the engineering bar: lead design and code reviews, direct the day-to-day work of engineers on your initiatives, and guide them through the most complex data and performance issues. Teach engineers to inspect and challenge AI-generated work, share reusable patterns and context through internal and external forums, and help managers identify strengths and development needs.