Data Engineer, Alternative Data | Delta One Trading | Experienced Hire

Overview

We are seeking a Data Engineer to join our Systematic Delta One desk. Engineers, researchers, and traders collaborate to develop scalable, fully automated trading strategies across liquid global products and venues. This role involves owning the path from raw vendor and alternative datasets to research-ready data that powers alpha research and signal development.

How You'll Make An Impact

• Own the end-to-end onboarding of new vendor and alternative datasets: from evaluating samples and data dictionaries with researchers, through building ingestion pipelines, to production monitoring
• Build point-in-time-correct datasets: preserving as-delivered history and handling vendor restatements, revisions, and backfills, so backtests see exactly what was knowable at the time
• Extend the platform beyond tabular feeds: apply LLMs and agentic tooling to extract structure from unstructured vendor material (documents, filings, transcripts, data dictionaries) and to automate onboarding, entity-resolution, and data-quality workflows
• Create research-ready datasets optimized for large-scale historical analysis and backtesting workflows
• Improve the shared ingestion platform and tooling so that each new dataset onboards faster than the last

What We're Looking For

• 5+ years of experience building Python data applications and pipelines over large historical datasets, with a performance-aware mindset
• Experience ingesting and normalizing third-party or vendor data at scale (batch feeds over S3/SFTP, cloud data shares such as Snowflake, or APIs)
• Strong SQL and familiarity with modern columnar and analytical tooling (Parquet, Arrow, DuckDB or similar), alongside NumPy, Pandas, or Polars
• A strong understanding of data modeling, data accuracy, and reproducible research workflows; experience with temporal or versioned data (point-in-time, slowly changing dimensions, bitemporal modeling) is preferred
• Experience operating production data pipelines: monitoring, alerting, backfill and restatement handling, incident forensics
• Ability to work closely with researchers and scientists, taking ambiguous ideas and evolving them into robust datasets and scalable workflows
• Experience applying LLMs to data problems (extraction, classification, entity resolution, data-quality checking) or building LLM-assisted and agentic tooling
• Experience with cloud data delivery (AWS S3, Snowflake) is a plus; prior experience in C++ is a plus
• Experience in quantitative finance or electronic trading environments is a plus but not required
• An advanced degree in Computer Science, Mathematics, Physics, Computer Engineering, or a related field is a plus

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. We apply machine learning and advanced quantitative research to vast datasets to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

What We Do

We are experts in trading essentially all listed financial products and asset classes, with a focus on derivatives trading. Through market making and market taking, we handle millions of trading transactions around the world every day, providing liquidity and ensuring competitive prices for buyers and sellers. While our presence in the market is broad, our trading desks are highly specialized, allowing for a deep understanding of unique drivers of each asset class.