Data Engineer, Alternative Data for Quant Trading

SIG; New York (NY); On-site; USD 225,000 - 250,000; Full time

Benefits offered by this job

Discretionary bonus

Job summary

Susquehanna is seeking a Data Engineer for its Systematic Delta One desk in New York. You will build scalable pipelines ingesting vendor and alternative data, ensuring time-correct datasets for alpha research and backtesting.

You'll collaborate with researchers to translate ideas into robust data architectures, applying LLMs to extract structure from unstructured sources and automate onboarding and quality checks.

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, or APIs).
• Strong SQL and familiarity with Parquet/Arrow/DuckDB, plus NumPy, Pandas, or Polars.

Responsibilities

• Own end-to-end onboarding of new vendor and alternative datasets from evaluation to production monitoring.
• Create point-in-time-correct datasets preserving history and handling restatements/backfills for accurate backtests.
• Design entity-mapping and reference datasets connecting vendor identifiers to tradable instruments.
• Extend the platform to handle unstructured data via LLMs and agentic tooling for onboarding and data quality workflows.
• Run data-quality and vendor-evaluation studies guiding trials and licensing decisions.
• Create research-ready datasets optimized for large-scale historical analysis and backtesting.

Skills

Python data pipelines

SQL

Parquet/Arrow/DuckDB

NumPy/Pandas/Polars

Data modeling

Production data pipelines

Cloud data delivery (AWS S3, Snowflake

LLM-assisted data tooling

Education

Advanced degree in CS/Math/Physics/Engineering

Tools

Snowflake

S3

DuckDB

Parquet

Job description

Susquehanna is seeking a Data Engineer for its Systematic Delta One desk in New York. You will build scalable pipelines ingesting vendor and alternative data, ensuring time-correct datasets for alpha research and backtesting.

You'll collaborate with researchers to translate ideas into robust data architectures, applying LLMs to extract structure from unstructured sources and automate onboarding and quality checks.