Acadian Asset Management LLC is a Boston-headquartered investment management firm with wholly owned affiliates located in Singapore, London, and Sydney. As of September 30, 2024, the firm managed approximately US$120 billion on behalf of major pension funds, endowments, foundations, governments and other investors based in the U.S. and abroad.

Position Overview: 

We’re looking for someone to help drive our data transformation efforts from the inside out. This isn’t about minor tweaks it’s about reimagining and rebuilding our data platform to keep us ahead in a rapidly evolving landscape. You’ll be using the latest tools like Apache Arrow, Polars, and Dagster, to shape a data architecture that’s as forward-thinking as our investment strategies.

This is a hands-on engineering management position, embedded within our investment team. You’re a key player in how we build long-lasting data pipelines that are reliable, supportable, adaptable, and fast. Moreover, you’ll lead efforts to create architecture that can be logically organized and easily taught to your team and others for rapid delivery and adoption. At Acadian, you’ll be at the cutting edge of systematic investing. Your work will matter—not just to your team but to the entire company and our clients. If you’re looking for a place where your skills and ideas will be valued and where you can push the boundaries of what’s possible, this is the team for you.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do:

• Assess our current data systems, identifying quick fixes, mid-term priorities, and long-term improvements.
• Build relationships with your team, Data Integration and Analysis, Data Platform Engineering, and key investment stakeholders.
• Establish a clear responsibility matrix for your team to ensure ownership and collaboration.
• Modernize outdated processes and leverage new data platform foundations to start building transitional architecture.
• Dive into existing systems to bridge legacy and new solutions seamlessly.
• Expand your team, mentor, and upskill members to meet evolving demands. 
• Map out the data landscape, aligning new pipelines with legacy systems for smooth integration.
• Collaborate closely with the Data Integration team to refine the transitional architecture and implement rapid prototyping.
• Deploy new data pipelines covering various data types, ensuring 15-20% of pipelines are rebuilt using the new architecture.
• Transition critical processes to modern platforms, maintaining high standards for data quality and performance.
• Work with diverse data sets, including benchmarks, holdings, and financial performance, while partnering with data vendors.

We’re Looking for Teammates With:

• Bachelors degree with an outstanding academic record in a technical field such as mathematics, science, or engineering.
• 10-15 years experience in quantitative finance and data management, ideally within a financial or tech-driven environment.
• Deep knowledge of modern data architectures and pipelines, including experience with Medallion architecture, data lakehouse, composable data systems architecture, and microservices, or familiarity with other architectural patterns.
• Familiarity with the Data Mesh architecture to enable decentralized data ownership and management.
• Leadership skills that go beyond just managing a team—you’re someone who can mentor and inspire.
• A real passion for building systems that are not just efficient but also scalable and reliable.
• You’ll need to understand how your work fits into the broader investment process—this isn’t just about tech, it’s about making sure we’re delivering the best possible outcomes for our clients. It’s about providing real impact for the business and driving returns.
• Experience in building a support model that meets consumer SLAs—understanding that data is a 24/7 business is crucial.
• You will be managing people across multiple offices, including in Singapore, so a global perspective and experience in managing distributed teams is essential.