Quant Data Engineer: Alternative Data for Alpha Research

SIG Susquehanna; New York (NY); Hybrid; USD 225,000 - 250,000; Full time

Job summary

SIG Susquehanna is seeking a Data Engineer for our Systematic Delta One desk, where engineers, researchers, and traders collaborate to develop scalable, automated trading data pipelines and research-ready datasets. The role focuses on ingesting third‑party data at scale, building end-to-end pipelines over historical data, and enabling backtests with accurate point‑in‑time history.

Strong Python, SQL, and data modeling skills are essential, with a bias toward reproducible research and efficient

Qualifications

• 5+ years of experience building Python data applications and pipelines over large historical datasets.
• 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.

Responsibilities

• 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

Skills

Python data pipelines

Data ingestion

SQL

NumPy/Pandas/Polars

Data modeling

Temporal data

Production pipelines ops

LLMs in data

AWS S3/Snowflake

C++

Finance/trading exp

Education

Advanced degree in CS/Math/Physics/Eng

Tools

Snowflake

DuckDB

Parquet

Job description

SIG Susquehanna is seeking a Data Engineer for our Systematic Delta One desk, where engineers, researchers, and traders collaborate to develop scalable, automated trading data pipelines and research-ready datasets. The role focuses on ingesting third‑party data at scale, building end-to-end pipelines over historical data, and enabling backtests with accurate point‑in‑time history.

Strong Python, SQL, and data modeling skills are essential, with a bias toward reproducible research and efficient