Vice President, Data Quality Platform

Responsibilities

• Build and scale data quality services and APIs that provide rule execution, scoring, and quality SLAs for datasets and domains
• Define and implement data quality frameworks: rules engine patterns, validation layers, quality gates, and exception workflows
• Establish data profiling and anomaly detection capabilities (completeness, distribution drift, outliers, schema drift)
• Implement reconciliation controls (source-to-target, balancing, row counts, sums/hashes, financial totals checks)
• Design metadata-driven quality rules: configurable rules, versioning, ownership, and auditable execution history
• Engineer quality controls across ingestion/processing:
• schema enforcement, type checks, nullability constraints
• referential integrity checks
• Support data lineage and traceability and data observability:
• Optimize high-volume query workloads in Snowflake/MSSQL while maintaining consistent quality checks at scale
• Partner with platform, governance, and product teams to drive long-term strategy for trusted data quality services
• Mentor engineers and influence architectural direction for enterprise-grade data quality platforms
• Required Qualifications
• 10+ years of backend/data platform engineering with strong hands-on development
• Deep expertise in Python and modern API frameworks such as FastAPI
• Strong experience building scalable services for data platforms (APIs, pipelines, controls)
• Strong hands-on experience with Snowflake and/or MSSQL, including performance tuning
• Proven experience implementing data quality controls across batch and/or streaming pipelines
• Experience with data quality operating models:
• quality SLAs/SLOs (freshness, accuracy, completeness)
• issue triage workflows, exception handling, and remediation
• Strong knowledge of data modelling (relational, dimensional, analytical) and model validation techniques
• Experience designing reconciliation frameworks and audit-grade controls (esp. for financial data)
• Familiarity with distributed systems, containerization (Open Container Initiative (OCI) container image packaging and runtime), and CI/CD pipelines
• Bachelor's/Master's in CS/Engineering or equivalent experience

Requirements

• Experience with data quality tools/frameworks (any of):
• Great Expectations, Soda, dbt tests, Bigeye(or equivalent in-house frameworks)
• Exposure to event-driven architectures (distributed event streaming and messaging platforms or similar) for near-real-time quality detection
• Experience with Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling and cloud-native platforms (AWS/Azure/GCP)
• Understanding of Domain-Driven Design (DDD), especially around data domains and ownership boundaries
• Technical Skills
• Python, FastAPI
• Snowflake, MSSQL
• Data Quality Framework Design (rules, scoring, gates)
• Data Profiling, Validation, Reconciliation
• Metadata-driven systems (rule catalogs, versioning, audit)
• Data Observability (freshness, volume, schema drift, SLAs)
• Data Lineage & Governance integrations
• Data Pipelines (ETL/ELT; batch/streaming)
• High-Volume Query Optimization
• Open Container Initiative (OCI) container image packaging and runtime / Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling, CI/CD

Benefits

Additional Information

About this role About this role Our Data Quality Platform is a critical component of the Aladdin Data ecosystem, ensuring that data powering investment analytics and business workflows is accurate, consistent, and trustworthy at scale. The platform provides a unified framework for data profiling, validation, reconciliation, observability, and governance across high-volume financial datasets.

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