Overview
We are hiring a senior data architecture leader to design and scale the core data platform underpinning a systematic trading and quantitative research environment.
This role is responsible for building the foundational systems that power research, simulation, and strategy development workflows. The platform must unify a wide range of market and reference datasets, transforming raw external inputs into clean, reliable, and analysis-ready formats that researchers and engineers can easily consume.
You will define how data is ingested, normalized, stored, and accessed across the organization, enabling fast iteration, reproducible results, and high-performance historical analysis. The ideal candidate combines strong distributed systems design experience with a deep understanding of the practical needs of quantitative researchers and trading teams.
Responsibilities
Architect and deliver a centralized research data platform that aggregates diverse market and reference datasets into a consistent and well-governed environment.
Design storage and retrieval systems optimized for large-scale historical analysis, simulation workloads, and low-latency research queries.
Build robust ingestion and transformation pipelines that standardize raw external data into structured, analysis-ready formats.
Develop internal APIs and tooling that allow researchers to quickly discover datasets, inspect metadata, and assemble inputs for modeling and testing.
Partner directly with quantitative researchers, data scientists, and engineers to understand workflow requirements and remove data friction from the research process.
Establish standards for schema design, normalization, lineage, validation, and version control to ensure accuracy and reproducibility.
Optimize for scalability and performance as both dataset breadth and computational demands increase.
Provide technical leadership on best practices for research data engineering and platform design.
Desired Experience
Experience building large-scale data systems within trading, quantitative research, or other time-series-heavy analytical environments.
Strong understanding of market data structures, historical datasets, and research/simulation workflows.
Expertise in data modeling, distributed storage systems, and high-throughput processing pipelines.
Track record of enabling researchers to run fast, iterative backtests or simulations on large historical datasets.
Ability to translate ambiguous research requirements into pragmatic, scalable infrastructure.
Comfortable operating as a senior technical owner with broad architectural responsibility.
