Senior Engineer, Systematic Data
The systematic data engineer builds acquisition and access systems that support alpha research. The platform preserves usable historical data while serving real-time and ongoing research needs.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Onboard and profile research datasets
- Design data-storage and retrieval APIs
- Improve time-series query efficiency and data availability
- Strengthen production quality and integration with research workflows
What they ask for
Required
- Seven or more years building production software
- Strong Python design
- Time-series databases and temporal queries
- Linux scripting
- Full-stack systems understanding
- Cash-equity instrument knowledge
- Front-office technology experience
Preferred
- Java or C#
- Distributed computing
- Cloud platforms
- Streaming and orchestration
- Education
- Computing or related computational qualification; degree level is unspecified.
- Experience
- 7+ years developing and supporting production systems, with front-office experience.
- Languages and tools
- Markets
- equities
How to prepare
- Demonstrate a time-series API with explicit as-of semantics
- Profile a dataset before exposing it to researchers
- Explain reliability controls for a production research-data platform
What to study
- Quantitative Data Engineering
- Python for Quantitative Finance
- Trading Systems and Low-Latency Infrastructure
The HFT Quant Developer roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Balyasny Asset Management’s site before applying: requirements, location and pay can change after we check them.
View the posting on Balyasny Asset Management’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Balyasny Asset Management and doesn’t handle applications.