Build and evolve a Data Quality Platform ensuring accurate, consistent, and trustworthy data at scale for investment analytics and business workflows
Job Summary
Build and evolve a Data Quality Platform ensuring accurate, consistent, and trustworthy data at scale for investment analytics and business workflows.
Develop scalable services and frameworks for proactive data quality measurement, enforcement, and monitoring across enterprise-wide high-volume financial datasets.
Partner with platform, governance, and product teams to drive long-term strategy for trusted data quality services and mentor engineers on architectural direction.
Matching Summary
Build and evolve a Data Quality Platform ensuring accurate, consistent, and trustworthy data at scale for investment analytics and business workflows.
Skills & Requirements
Must-have
Data quality services and APIs
Data quality frameworks design
Data profiling and anomaly detection
Reconciliation controls implementation
Metadata-driven quality rules
High-volume query optimization
Nice-to-have
Event-driven architectures
Cloud-native platforms
Domain-Driven Design understanding
Key Requirements
10+ years backend/data platform engineering
Strong hands-on Python and FastAPI
Experience building scalable data platform services
Hands-on Snowflake and/or MSSQL
Experience implementing data quality controls
Experience with data quality operating models
Strong data modelling knowledge
Experience designing reconciliation frameworks
Familiarity with distributed systems, Docker, CI/CD