About the role

The Lead – DataQuality and ReliabilityEngineering will architect and build the infrastructure, processes,technologyand collaboration model thatembeds rigorous data quality and reliability practices to ensure that data across all systems – from ingestion through consumption – isaccurate, consistent, and trustworthy.Theywill spearhead the development of our next-generationdata ecosystemwith a quality-first mindset, helpingshape 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 positiondemands a balanceofhands-on execution, strategic direction, and team leadershipto ensure areliable, high-quality data platformthatinstilsconfidence in analytics, reporting, and decision-making across the firm.

Experience & Education

• 10+ years inat scaledata 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 marketsdomain knowledgeis highly accretive to the role.

• Proficiencyin data architecture protocols andpreviousinvolvement with financial or investment data systems is beneficial.

• ABachelor’sdegree or equivalent experience in Computer Science, Engineering, or related discipline isa must.
  

Success Metrics

• Reduction in data quality incidentsandMTTRs.

• Automated coverage of quality checks across pipelines.

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

• Improvementsin end-to-end Data Lineage reliability.
  

Required Skills

Technical

• Establish data quality frameworksthat 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, andimplement asynchronous large-scale monitoring for batch data flows.

• Manage thelifecycle of data quality models– including regular retraining, version control, and performance monitoring – to ensure quality checksremaineffective as data evolves.

• Establish and publishdata 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 modellifecyclefor 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,establishhigh 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!