Investment Data & Analytics Engineer- Vanguard Personalized Indexing

Vanguard•Oakland, CA

7d•$90,000 - $150,000

About The Position

Investment Data & Analytics Engineer- Vanguard Personalized Indexing Key Responsibilities Investment Analytics & Tooling Design, build, and maintain Python‑based analytics tools that support investment research, portfolio analysis, and operational workflows. Develop interactive analytics applications and dashboards using Python analytics frameworks (e.g., Plotly Dash or similar). Partner with investment and research stakeholders to translate analytical needs into scalable, production‑ready solutions. Enable rapid experimentation while ensuring code quality, reliability, and maintainability. Investment Data Platform & Engineering Contribute to the design and evolution of investment data lakes and data pipelines on AWS. Build and maintain data ingestion, transformation, and access layers that support analytics and downstream investment use cases. Apply sound data engineering practices to ensure data quality, consistency, and observability. Collaborate with platform and architecture teams to align analytics solutions with VPI’s broader data strategy. Cloud‑Native Development (AWS) Develop and operate analytics and data solutions on AWS, leveraging managed services where appropriate. Build cloud‑native, scalable components that integrate with the broader VPI platform. Participate in CI/CD practices, automated testing, and operational readiness for analytics workloads. Production Support & Team Engagement Participate in support and operational activities, including investigation and resolution of analytics or data‑related issues. Contribute to on‑call or support rotations as required, with a focus on learning and continuous improvement. Actively engage in team ceremonies, design discussions, and code reviews. Continuously improve engineering practices, documentation, and reliability of analytics solutions.

Requirements

3+ years of professional experience in software engineering, data engineering, or analytics engineering.
Strong proficiency in Python, including experience with data and analytics libraries.
Experience developing analytics or visualization solutions using frameworks such as Plotly Dash (or comparable Python‑based tools).
Hands‑on experience working with AWS‑based platforms.
Familiarity with data engineering concepts such as data lakes, pipelines, schemas, and data quality.
Exposure to investments, finance, or capital markets through academic coursework or professional experience.
Ability to operate as a hands‑on individual contributor while collaborating effectively within a team.

Nice To Haves

Experience building or working with investment or financial datasets (e.g., market data, portfolios, transactions).
Familiarity with portfolio analytics, investment research workflows, or quantitative analysis.
Experience with data lake architectures and analytical data stores.
Exposure to JVM‑based languages such as Java.
Experience supporting production analytics systems in a regulated or high-availability environment.

Responsibilities

Design, build, and maintain Python‑based analytics tools that support investment research, portfolio analysis, and operational workflows.
Develop interactive analytics applications and dashboards using Python analytics frameworks (e.g., Plotly Dash or similar).
Partner with investment and research stakeholders to translate analytical needs into scalable, production‑ready solutions.
Contribute to the design and evolution of investment data lakes and data pipelines on AWS.
Build and maintain data ingestion, transformation, and access layers that support analytics and downstream investment use cases.
Apply sound data engineering practices to ensure data quality, consistency, and observability.
Develop and operate analytics and data solutions on AWS, leveraging managed services where appropriate.
Build cloud‑native, scalable components that integrate with the broader VPI platform.
Participate in CI/CD practices, automated testing, and operational readiness for analytics workloads.
Participate in support and operational activities, including investigation and resolution of analytics or data‑related issues.
Contribute to on‑call or support rotations as required, with a focus on learning and continuous improvement.
Actively engage in team ceremonies, design discussions, and code reviews.
Continuously improve engineering practices, documentation, and reliability of analytics solutions.