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Metro location and general data engineering skills increase competition, but quant focus narrows candidate pool.
Role requires quant/market-microstructure expertise, making skills less transferable across industries.
Explicit 1-4 year requirement plus mandatory Python, Postgres, Airflow and applied-statistics skills increases screening rigor.
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Design, build, and optimize high-throughput data pipelines handling tick-by-tick market and execution data.
Maintain mission-critical data infrastructure ensuring 24/7 data availability and high pipeline health with automated quality checks.
Collaborate with senior quants and product managers to translate complex transaction cost analysis research into production-grade features and client analytics.
1-4 years of relevant work experience.
BTech/MTech degree in Computer Science, Data Science, Statistics, or related quantitative field.
Expertise in Python (Numpy, Pandas, Polars, Multiprocessing) and advanced PostgreSQL (complex joins, window functions, query optimization).
Experience with data pipeline orchestration tools such as Airflow, Dagster, or Prefect.
Comfortable taking end-to-end ownership of data products and working in high-stakes, real-time trading environments.
Strong statistical background with ability to analyze order flow and validate results statistically, suitable for fintech or electronic trading data-heavy research.
Experienced in collaborating closely with senior quantitative researchers and product managers, capable of translating technical/statistical findings into actionable insights.