Data Quality Engineer
Position Summary: Data Integrity Engineer
Join a dynamic team focused on safeguarding the accuracy and reliability of financial market data. In this role, you'll contribute to the development and maintenance of systems that validate and monitor both historical and live data streams. Your work will directly support mission-critical trading and risk operations by ensuring data fidelity across various asset classes.
Primary Duties
- Monitor and assess the integrity of market data across multiple levels (L1, L2, L3) for equities and futures.
- Build and refine automated validation tools tailored to specific asset types.
- Ensure consistency and correctness in both archived and real-time datasets.
- Collaborate in the early detection of anomalies through active pipeline surveillance and alerting mechanisms.
- Investigate discrepancies and coordinate with internal teams and external data providers to resolve issues.
- Maintain thorough records of data quality metrics, incidents, and resolutions.
- Adopt and apply industry best practices in data validation and automation.
Required Skills & Experience
- 3-5 years of experience in data engineering, data validation, or a closely related discipline.
- Familiarity with market data feeds (L1-L3) for equities and/or futures; experience with providers like Bloomberg or Refinitiv is advantageous.
- Strong Python skills for scripting, analysis, and automation.
- Understanding of ETL workflows and data pipeline architecture.
- Excellent analytical thinking and problem-solving capabilities.
- Effective communicator with a proactive learning mindset.
Preferred Attributes
- Experience working with cloud-native data infrastructure or distributed computing systems.
- Knowledge of frameworks for processing streaming data.
- Interest in financial markets or prior exposure to the finance industry.
FAQs
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