DRW

Financial Services

EquitiesDataEngineer

$175–200k Chicago, Illinois, United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Equities Data Engineer at DRW. Skills: Data engineering, Data pipelines, Financial data. Partner with traders, researchers, and analysts to deliver. Build resilient pipelines for cleaning, validating, and transforming”

Industry & Context.

Financial Services
Problems you'll solve

Anomaly detection; Troubleshooting

What They're Looking For.

Must Have

Over five years of experience designing ingestion pipelines, Experience processing real-time and batch financial market data, Proven ability to work in an agile, fast-paced environment, Handle trading environment demands, Understanding of financial point-in-time and time-series data and analysis, Proven expertise in developing data quality control processes, Experience with monitoring, observability, and alerting systems for data pipelines, Competent in on-premise Linux systems, Competent in cloud platforms, Proficient in automated testing, Proficient in CI/CD practices, Proficient in MLOps, Well-versed in compressed and optimized file formats, Experience with Parquet, Experience with Iceberg

Nice to Have

Familiarity with equities, equity indices, futures, or delta one trading data preferred

What You'll Do.

and analysts to deliver

Build resilient pipelines for cleaning

Automate resilient pipelines for cleaning

Maintain resilient pipelines for cleaning

Develop observability tools for pipeline reliability and performance

Develop monitoring tools for pipeline reliability and performance

Develop alerting tools for pipeline reliability and performance

Optimize tiered data storage

Optimize elastic processing

Enforce data governance standards

Enforce data controls

Enforce data security standards

Implement data quality frameworks

Detect staleness in data

Detect corporate action inconsistencies

Detect market data inconsistencies

Maintain point-in-time correctness across datasets

Ensure reproducibility of signals

Ensure reproducibility of backtests

Collaborate with platform/infrastructure teams to productionize pipelines

Improve runtime efficiency

Meet latency requirements

Meet availability requirements

How You'll Work.

Team & Collaboration

With traders; With researchers; With quantitative developers; With platform teams; With infrastructure teams

Process & Methodology

Agile

Full Job Description

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. We are seeking an Equities Data Engineer to join the MASS (Multi-asset Systematic Strategies) trading team. In this role, you will be responsible for onboarding, transforming, and managing diverse financial datasets. You will collaborate closely with traders, researchers, and quantitative developers to analyze equity and futures data, identify alphas, and develop global delta-one trading strategies. Key Responsibilities: Partner with traders, researchers, and analysts to deliver well-structured data that powers trading strategies, predictive models, and AI/ML applications. Build, automate, and maintain resilient pipelines for cleaning, validating, and transforming batch and streaming data that feed into medallion architectures. Develop observability, monitoring, and alerting tools to provide complete visibility into pipeline reliability and performance. Optimize tiered data storage and elastic processing across on-prem, cloud, and hybrid environments to ensure scalable and cost-effective solutions. Enforce data governance, controls, and secu

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