Portfolio Analytics Engineer

About the role

You will own the design and delivery of analytics platforms that power critical decision-making across AQR's investment workflows. This role centers on translating complex research into robust, production-grade analytical systems that uphold the firm's commitment to intellectual honesty. You will operate with deep autonomy, challenging assumptions and isolating what truly matters in market behavior through rigorous analytical thinking. Collaboration will be central as you work alongside researchers and portfolio managers to ensure transparency and openness lead to meaningful innovation. You will mentor engineers and set a high bar for quality, using data to drive reliability and performance. Your work will directly support the firm's mission of delivering superior, long-term results by leveraging technology to turn insight into action. You will be responsible for maintaining the integrity and scalability of systems that underpin some of the firm's most sophisticated investment strategies.

Key facts

What you'll do

• Architect and implement analytics platforms that ingest research outputs, perform constrained optimizations, and generate actionable investment signals.
• Design, build, and operate distributed, microservice-based analytical systems that scale reliably under high volume and strict latency requirements.
• Lead architectural trade-off analysis for high-performance, high-availability systems, balancing flexibility, maintainability, and execution speed.
• Drive cross-functional initiatives that span multiple services, data domains, and engineering teams to delivery end-to-end solutions.
• Provide technical leadership to small engineering pods, planning tasks, conducting design reviews, and overseeing execution against demanding timelines.
• Mentor and coach engineers through hands-on code reviews, design discussions, and pragmatic guidance that elevates overall team capability.
• Partner with researchers, portfolio managers, and global engineers to translate investment hypotheses into production-quality analytical solutions.
• Instrument systems with observability and monitoring platforms, using metrics and traces to diagnose issues and improve reliability.
• Own the full lifecycle of analytical features, from requirements capture and technical specification through implementation, testing, and rollout.
• Apply rigorous engineering practices, including automated testing, peer review, and documentation, to ensure robust and maintainable code.
• Profile and tune high-performance analytical pipelines to meet strict operational standards in production environments.
• Contribute to estimating effort, planning delivery, and managing technical trade-offs in alignment with business priorities and risk constraints.
• Evaluate emerging tools and techniques to enhance analytical throughput, reduce latency, and improve insight quality.
• Communicate complex analytical concepts clearly to both technical and non-technical stakeholders, ensuring alignment on objectives and outcomes.

Requirements

• Hold a degree in Computer Science or a closely related technical field that provides foundational knowledge of algorithms, systems, and data structures.
• Bring 7 or more years of professional software engineering experience, with deep expertise in Java as a primary implementation language.
• Demonstrate strong experience using Python in production or research-adjacent environments to support analytics and workflow automation.
• Show a proven track record of designing, building, and operating large-scale, distributed systems that meet stringent reliability and performance goals.
• Have hands-on experience with microservice-based architectures and related technologies, including Java, Elasticsearch, Redis, RabbitMQ or AMQ, and relational databases such as PostgreSQL or MS SQL Server.
• Adhere strictly to engineering best practices, including code quality, clear design documentation, thorough peer code reviews, automated testing, and measurable test coverage.
• Operate and tune high-performance systems in production, applying profiling and diagnostic techniques to resolve complex issues quickly.
• Use observability and monitoring platforms, such as New Relic, to drive reliability and performance improvements through data-informed decisions.
• Lead technical change initiatives, manage risk, and guide projects to completion even in complex, ambiguous, and fast-paced environments.
• Participate actively in requirements capture, technical specification, and work breakdown, ensuring clarity and alignment across teams.
• Estimate work accurately, plan delivery schedules, and navigate technical trade-offs while respecting constraints and stakeholder expectations.
• Maintain a strong interest in financial markets and the investment process, enabling deeper collaboration with investment professionals.

Nice to have

Only include preferred items if they are explicitly stated in the source material.

Practical notes

The engagement type is indicated as "See source," and details regarding hours, travel, visa requirements, and application deadlines are only included when explicitly stated in the source. No additional information beyond the source content is added.