IT Data Quality Lead Engineer

Role Overview:

As a Data Quality Engineering Lead (Global Markets) in the Markets Division of a leading global bank, you will ensure the accuracy, consistency, and reliability of data across trading, risk management, regulatory reporting, and financial analytics systems worldwide.

This role requires strong domain expertise in capital markets, trading systems, risk analytics, and global regulatory compliance. You will work with cross-functional teams across multiple geographies (APAC, EMEA, NAM) to establish enterprise-wide data quality frameworks that enhance trading operations, support risk mitigation, and ensure compliance with regional and global financial regulations.

Your work will directly impact the bank’s ability to manage financial risk, optimize trading strategies, and meet the stringent requirements of regulatory bodies across different markets.

Key Responsibilities:

• Develop and enforce global data quality frameworks for front-office, middle-office, and back-office systems supporting multi-asset class trading.
• Ensure data integrity across global trading platforms, pricing models, risk engines, and regulatory compliance systems.
• Implement automated data validation for real-time market data, trade transactions, and post-trade analytics.
• Collaborate with trading desks, quants, risk, and compliance teams across different time zones to resolve data anomalies.
• Work closely with data engineering and DevOps teams to embed data quality checks in CI/CD pipelines for front-office trading applications.
• Define and monitor key data quality metrics (accuracy, completeness, consistency, timeliness) across global financial systems.
• Establish data lineage, metadata management, and governance frameworks for high-quality regulatory reporting.
• Ensure compliance with global and regional regulatory frameworks.
• Lead initiatives on AI/ML-based data anomaly detection, improving real-time insights for market risk and pricing data.
• Provide thought leadership in data observability, automated testing, and cloud-based data quality solutions.

Key Skills & Qualifications:
Domain Knowledge:

• Strong understanding of global markets trading infrastructure, including order management systems (OMS), execution management systems (EMS), market data feeds, and post-trade settlement systems.
• Knowledge of multi-asset classes: equities, fixed income, FX, commodities, derivatives, structured products.
• Knowledge of financial risk modeling, P&L attribution, VaR calculations, and liquidity risk metrics.

Technical Expertise:

• Proficiency in Java, Python, SQL for data quality automation and testing.
• DevOps & Automation: Experience embedding data quality checks into DevOps CI/CD pipelines.
• Experience working in AWS, Azure, or GCP data services.
• Experience with data quality tools (such as Great Expectations, Monte Carlo, Talend, Collibra, Informatica DQ) is a plus.
• Experience with big data platforms (such as Snowflake, Databricks, Spark, Hadoop) is a plus.
• Experience with data pipeline orchestration tools (such as Airflow, DBT, Prefect) is a plus.

 Soft Skills:

• Ability to manage global stakeholders, including traders, quants, risk managers, and compliance teams across multiple regions.
• Strong problem-solving and decision-making skills in a fast-paced trading environment.

Preferred Qualifications:

• Degree in Computer Science, Software/Computer Engineering, or a related field.
• Experience with real-time market data validation and anomaly detection.

• Experience in AI/ML-driven anomaly detection for trading and risk data quality.

Experience: 10+ years in Data Quality Engineering, Data Governance, or Data Testing within a global banking or capital markets environment.

Why Join Us?

• Work at a global scale: Collaborate with teams across APAC, EMEA, and NAM in a leading investment bank.
• Lead data quality initiatives that drive regulatory compliance, risk mitigation, and market efficiency.
• Engage with cutting-edge trading technology and AI-driven data quality automation.
• Shape the future of data governance in an era of high-frequency trading and real-time analytics.

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