Data Quality and Reliability Engineering, BlackRock Global Markets, Director

About the role 

The Lead – Data Quality and Reliability Engineering will architect and build the infrastructure, processes, technology and collaboration model that embeds rigorous data quality and reliability practices to ensure that data across all systems – from ingestion through consumption – is accurate, consistent, and trustworthy. They will spearhead the development of our next-generation data ecosystem with a quality-first mindset, helping shape a platform that is not only scalable and innovative, but also delivers trusted data for critical financial and analytical use cases. As a leader, this position demands a balance of  hands-on execution, strategic direction, and team leadership to ensure a reliable, high-quality data platform that instils confidence in analytics, reporting, and decision-making across the firm. 
 

Experience & Education 

• 10+ years in at scale data quality, data science, or ML engineering. Experience operationalizing ML or rules-based quality systems at enterprise scale. Deep understanding of data validation, anomaly detection, and data observability.  

• Investment Systems, Capital markets domain knowledge is highly accretive to the role. 

• Proficiency in data architecture protocols and previous involvement with financial or investment data systems is beneficial. 

• A Bachelor’s degree or equivalent experience in Computer Science, Engineering, or related discipline is a must. 
   

Success Metrics 

• Reduction in data quality incidents and MTTRs. 

• Automated coverage of quality checks across pipelines.  

• Measurable improvement in data reliability metrics (accuracy, timeliness, consistency).  

• Improvements in end-to-end Data Lineage reliability. 
   

Required Skills 

Technical 

• Establish data quality frameworks that combine multiple approaches: rules-based validation (schema checks, business rules, thresholds), statistical profiling (distribution checks, drift detection), machine learning models for anomaly detection, and LLM-assisted contextual checks for semantic data validation. 

• Integrate these validation models into data pipelines for real-time (synchronous) checks, and implement asynchronous large-scale monitoring for batch data flows. 

• Manage the lifecycle of data quality models – including regular retraining, version control, and performance monitoring – to ensure quality checks remain effective as data evolves. 

• Establish and publish data quality metrics and dashboards (e.g. completeness, timeliness, accuracy, consistency) to provide a transparent, measurable view of data health across the platform. 

• Embed robust observability hooks and automated remediation processes into data pipelines, so that data issues are detected early and addressed proactively without manual intervention. 
   

Leadership & Strategy 

• Develop governance around model lifecycle for data quality — from training to deployment.  

• Define and track KPIs for data quality and reliability.  

• Partner with Data Platform Engineering, data governance, and analytics leads to embed quality monitoring throughout data flows.  

• Demonstrated ability to lead and mentor engineering teams, establish high coding standards, and drive continuous improvement in platform reliability and developer efficiency. 

• Excellent collaboration and communication skills, with comfort in bridging technical and business needs to deliver results!