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Mid-level data engineer role with quant specialization reduces broad applicant pool but still moderately competitive.
Quantitative trading domain specificity and production trading experience make background transferability limited.
Explicit 5+ years, mandatory Python/SQL/data engineering production experience and reliability requirements increase filter strictness.
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Own and maintain end-to-end automated data pipelines and ETL workflows critical to live trading systems.
Drive improvements in reliability, scalability, and operational efficiency of data infrastructure supporting quantitative research and trading operations.
Investigate and resolve production issues, ensuring high availability and data quality for large-scale financial datasets.
5+ years of experience in Data Engineering, Quantitative Engineering, or Data Operations.
Strong Python programming skills with experience in Pandas, Polars, NumPy, and PyArrow.
Expertise in SQL and database design (PostgreSQL, MySQL, MSSQL) plus Linux and shell scripting proficiency.
Experience designing and maintaining ETL/data pipelines in high-reliability production environments.
Experienced working in quantitative finance or live trading environments with large, complex financial datasets.
Skilled in collaboration across researchers, traders, and engineers to deliver robust data solutions under fast-paced conditions.
Demonstrates expertise in root-cause analysis and proactive incident management of mission-critical systems.