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# Equities Data Engineer

- **Company:** D R W Inc
- **Location:** New York, United States
- **Experience:** Expert
- **Salary:** $175,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Continuous Integration, Data Governance, Linux, Machine Learning, Data Streaming, Parquet, Data Storage Technologies, Data Ingestion, Machine Learning Operations, Data Pipelines
- **Published:** July 28, 2026
- **Apply:** https://www.dice.com/job-detail/8dfd123b-134b-4fbf-9a7f-7ae750ec45af

## About the Role

* Over five years of demonstrated experience designing ingestion pipelines.
 * Familiarity with equities, equity indices, futures, or delta one trading data preferred.
 * Experience processing real-time and batch financial market data.
 * Proven ability to work in an agile, fast-paced environment and handle trading environment demands.
 * Strong understanding of financial point-in-time and time-series data and analysis.
 * Proven expertise in developing data quality control processes to detect gaps or inaccuracies.
 * Experience with monitoring, observability, and alerting systems for data pipelines.
 * Competent in both on-premise Linux systems and cloud platforms.
 * Proficient in automated testing, CI/CD practices, and MLOps.
 * Well-versed in compressed and optimized file formats such as Parquet and Iceberg.

## Description

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., * 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 security standards to preserve confidentiality and operational integrity.
 * Implement data quality frameworks (validation, reconciliation, anomaly detection) to detect gaps, staleness, and corporate action/market data inconsistencies.
 * Maintain point-in-time correctness across datasets used for research and live trading; ensure reproducibility of signals and backtests.
 * Collaborate with platform/infrastructure teams to productionize pipelines, improve runtime efficiency, and meet latency and availability requirements.


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