OverviewWe are seeking a Data Engineer to join our Systematic Delta One desk, where engineers, researchers, and traders work side-by-side to develop scalable, fully automated trading strategies across liquid global products and venues. Partnering closely with our quantitative researchers, this role owns the path from raw vendor and alternative datasets to the point-in-time-correct, research-ready data that powers alpha research and signal development.
The ideal candidate combines strong Python engineering skills with hands-on experience ingesting and normalizing third-party data at scale: batch feeds over S3 and SFTP, cloud data shares, and APIs, and increasingly semi-structured and unstructured sources such as documents, transcripts, and text. You will design pipelines and research tools that process billions of rows of historical data efficiently, reproducibly, and with a high degree of correctness.
A core part of the role is translating evolving research ideas into usable datasets and research infrastructure, working with our market-data and compliance teams during vendor trials. Success in this role requires strong communication skills, intellectual curiosity, and the ability to iterate quickly as hypotheses and data requirements evolve.
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
- Design entity-mapping and reference datasets that connect vendor identifiers (brands, merchants, estimate line items) to tradable instruments
- 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
- Run data-quality and vendor-evaluation studies (coverage, revision behavior, panel stability) that directly inform trial and licensing decisions
- 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
- 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) strongly preferred
- Demonstrated success 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 is a plus
- 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
What you can expect from us: Real Impact: You will onboard the datasets that decide which signals get built, and see your pipelines feed research and production trading directly. Your work makes the whole research organization smarter, faster, and better.
Collaboration: Our data engineers, researchers, and traders work together daily; the feedback loop from a dataset you built to a strategy in production is short and visible.
Growth: We're looking for people who are naturally curious, relentless problem solvers, and have the desire to continuously innovate, learn, and grow; prior proprietary-trading experience is not required.
About SusquehannaSusquehanna 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. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order 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.
If you're a recruiting agency and want to partner with us, please reach out to Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
The annual base pay range for this role is $225,000 - $250,000 + discretionary bonus + benefits. Susquehanna considers factors such as scope and responsibilities of the position, work experience, education/training, key skills, as well as market and organizational considerations when extending an offer.
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