Director of Data Architecture and Engineering
HometapFull Time
Expert & Leadership (9+ years)
Key technologies and capabilities for this role
Common questions about this position
The role requires 10+ years in at scale data quality, data science, or ML engineering, with experience operationalizing ML or rules-based quality systems at enterprise scale.
Key skills include establishing data quality frameworks with rules-based validation, statistical profiling, ML models for anomaly detection, integrating validations into pipelines, managing model lifecycles, and creating data quality metrics and dashboards.
A Bachelor’s degree or equivalent experience in Computer Science, Engineering, or related discipline is required.
Investment Systems, Capital markets domain knowledge is highly accretive to the role, and proficiency in data architecture protocols with previous involvement in financial or investment data systems is beneficial.
Success is measured by reduction in data quality incidents and MTTRs, automated coverage of quality checks across pipelines, measurable improvement in data reliability metrics, and improvements in end-to-end Data Lineage reliability.