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# Data Engineer

- **Company:** InTec, LLC
- **Location:** Chicago, IL, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Information Engineering, Data Infrastructure, Data Structures, Python (Programming Language), Data Ingestion, Data Management, Data Pipelines
- **Published:** October 16, 2026
- **Apply:** https://www.nettemps.com/us/en/W917F333991F08962D5.ntap

## About the Role

* 3+ years' commercial experience in Data Engineering, ideally within proprietary trading, hedge funds or financial markets.
 * Strong Python skills, including experience with its data science ecosystem.
 * Proven experience designing and implementing scalable data ingestion and transformation pipelines.
 * Good understanding of options, futures and derivatives, including their associated market data requirements.
 * Strong knowledge of financial time-series and point-in-time data.
 * Experience working with financial market data vendors and integrating external datasets.
 * Proven ability to implement data quality, validation and monitoring processes.
 * Strong analytical and problem-solving skills with the ability to operate in a fast-paced trading environment.
 * Excellent communication skills and experience collaborating with trading, research and engineering teams.

## Description

The successful candidate will work closely with traders, quantitative researchers and developers to build scalable data infrastructure supporting options and delta-one trading strategies. This is an excellent opportunity to work in a high-performance trading environment where data quality, accuracy and accessibility are critical to business success.

Role & Responsibilities

 * Design, develop and maintain scalable data ingestion pipelines and core trading datasets.
 * Onboard, transform and integrate financial data from internal systems and external vendors.
 * Build robust data quality controls to identify gaps, inconsistencies and inaccuracies.
 * Collaborate with traders, quantitative researchers and developers to support trading strategies and research initiatives.
 * Develop and optimise historical, point-in-time and time-series datasets.
 * Implement monitoring, observability and alerting solutions across data pipelines.
 * Enhance data accessibility through efficient data structures and APIs.
 * Support AI-driven initiatives through the annotation and enrichment of financial datasets.