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Capital Group

Asset management with long-term portfolios

Data Engineer Lead

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • At least 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, with understanding of the security, access, and cost implications of design decisions.
  • Experience orchestrating production pipelines with Apache Airflow, including Astronomer, and building 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.
  • Experience implementing 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.
  • Ability to use AI coding agents beyond code completion by writing specifications, supplying context, running tests, and reviewing generated changes through source control.
  • Ability to evaluate AI-generated output with representative test cases, regression tests, execution traces, and human review, and distinguish a plausible answer from a verified one.
  • Understanding of prompt injection, sensitive data handling, and least-privilege access, and ability to design approval boundaries and audit trails for agents working against enterprise systems.
  • Ability to lead architecture discussions, influence without formal authority, develop other engineers, and explain technical choices and trade-offs clearly to investment professionals and technology leaders.
  • A bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

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; influence standards and practices across adjacent teams and the wider Capital Group technology organization; and contribute 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 by translating business outcomes into well-defined specifications, engineering the business, architectural, and repository context agents need, and directing 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 autonomously, 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 platform layers, 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.
  • Lead design and code reviews, direct the day-to-day work of engineers on initiatives, and guide them through 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.

Desired Qualifications

  • 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.

About the Company

Capital Group

Asset management with long-term portfolios

Capital Group is a private investment firm managing equity and fixed-income assets for millions of investors. It builds long-term, high-conviction portfolios using rigorous research and the American Funds lineup to pursue steady, durable results rather than short-term trades. It differentiates itself through strong personal accountability, a globally distributed team, and a research-driven investment process. Its goal is to improve people’s lives through successful investing.

Company size
5,001-10,000
Company stage
Not listed
Total funding
$65.4B
Headquarters
Dongcheng District, China
Founded
Not listed

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