Designing, implementing, and enhancing event log aggregation and transformation architectures (fulfilling both real-time and batch SLAs) in order to deliver timely, accurate, and relevant information to downstream systems responsible for user visualizations, ad-exchange bidding, billing, data analytics, and data management.

Architecting solutions to ensure that data-transformation systems continue to provide business value following changes required to comply with user-privacy regulations (e.g. GDPR).

Optimizing distributed-processing logic (particularly on Spark) and cloud-deployment topologies (particularly on AWS) to meet client SLAs and reduce cloud-infrastructure outlays.

Engaging adjacent platform teams (particularly those responsible for bidding and user experience) in order to identify new ways to extract actionable insights from source data, then implementing the resulting processes and integrating them into the data pipeline.

Researching and implementing distributed, cloud-based data processing architectures capable of distilling patterns from raw signal and tick data, with a view to the development of new

quantitative trading strategies.

Designing and building back-end systems to facilitate the execution of investment strategies

deployed by Hywin portfolio managers.

Automating the acquisition of raw trading data from disparate sources and ensuring the timely

and reliable distribution of said data internally.

Designing and implementing microservice components integral to the foundation of Liaison's data management platform offering.

Collaborating with other software engineers and architects in the context of a self-­directed, cross-functional team, to ensure that components and services are implemented, tested, and deployed in a "fail­-fast" manner which allows for rapid response to reported issues.

Researching new technologies (particularly in the open­-source arena) which might prove beneficial to the platform, and constructing prototypes to demonstrate their utility.

Building and iterating upon the Mesos­-based distributed-­deployment framework designed to allow data platform services to achieve high levels of uptime while also permitting frequent code updates via rolling deployment.

Providing technical leadership to affiliated Liaison software development teams, in order to help them infuse cutting­-edge technologies prototyped by the Data Management Development team into their products, and to assist them in integrating their platforms into Liaison's overall data management strategy.