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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

What Success Looks Like

• You are actively building Python‑based analytics tools that enable investment research and decision‑making.

• You help strengthen the investment data foundation that underpins VPI’s analytics and platform capabilities.

• You balance rapid analytical development with production‑quality engineering practices.

• You collaborate closely with engineers, investment partners, and platform teams to deliver meaningful business impact.

• You grow your technical depth while contributing reliably to a mission‑critical investment platform.

Special Factors

Sponsorship

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

Future of Work

During the pandemic, we transitioned to a work from home model for the majority of our crew and we continue to interview, hire, and on-board future crew remotely.

As we have developed the path forward, we have taken a thoughtful approach that both maximizes the advantages of working remotely and the many benefits of coming together and collaborating in a shared workspace. We believe that in-person interactions among our crew are important for preserving our unique culture and advantageous for the personal development of our crew.

When our Crew return to the office, many will work in our hybrid model. A smaller proportion of our crew will operate in the Work from Home work model (for example, field sales crew); or in the Work from Office model (for example, portfolio managers).

The working model that your role falls into will be communicated to you in the interview process – please do ask if you are unsure. We encourage you to make the decision regarding your job interview and offer knowing which model your role will fall into. We will test and learn as our ways of working evolve and will continue to evaluate working models along the way.

Salary Range: