Investment Engineering is part of the Investments Team within Dimensional. Investment Engineering is responsible for ownership of investment data, which means managing data from acquisition through distribution, driving analysis to create information from data, and creating the information and analysis consumed by internal and external clients and reports. Investment Engineering is a hub group touching numerous areas of the implementation of the investment process and interacting with most other departments within Dimensional. The Analytics Platforms team creates tools for clients to understand Dimensional’s products and analyze how they might fit inside a client’s portfolio.

The foundation of all Analytics Platforms products is the Data Platform and serves as the data and computational engine of this ecosystem. This team is responsible for architecting and operating the centralized data platform that powers downstream analytics applications, feeds client-facing digital products, and enables self-service, ad hoc exploration for investment professionals.

Responsibilities

• Lead the design, build, and evolution of a highly scalable, secure, and resilient data platform architecture that unifies multi-source investment data for batch and real-time processing.

• Architect data infrastructure optimized to simultaneously serve high-concurrency client-facing digital tools and low-latency internal analytics applications.

• Implement robust data patterns, discovery tools, and optimized compute layers to allow investment professionals and business units to run complex ad hoc queries efficiently without platform degradation.

• Manage, mentor, and grow a team of high-performing investment engineers, maintaining a hands-on presence by participating in system design, writing core framework code, and conducting critical code reviews.

• Establish comprehensive data quality frameworks, lineage tracking, and security controls to guarantee trust and compliance across all data products.

• Collaborate with downstream application teams, product managers, and technology executives to define data contracts, SLAs, and infrastructure roadmaps that support company-wide data monetization and analysis.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Distributed Systems, or similar technical discipline. Relevant experience considered in lieu of specific degree.

• 10+ years of hands-on data engineering experience, with a proven track record of architecting enterprise data platforms

• Deep expertise in production Python, advanced SQL tuning, orchestrators (e.g. Airflow), comprehensive testing, and data transformation frameworks (e.g. dbt).

• Strong experience designing robust APIs and streaming architectures (e.g. Kafka) to seamlessly serve data to external-facing web applications.

• Master-level understanding of data warehousing concepts, semantic layer design, and diverse modeling methodologies tailored for financial markets or portfolio analytics.

• 3+ years of direct engineering management experience, with a demonstrated ability to recruit top talent, interface with centralized Technology teams, and balance technical debt with feature delivery.

• Familiarity with institutional investment datasets (e.g., security master, corporate actions, fundamental data, returns, or market data feeds from vendors like Bloomberg, FactSet, or MSCI).

This role is not eligible for immigration sponsorship.