Vice President, Data Quality Lead Engineer

About this role BlackRock is seeking a Data Quality Framework Lead to lead the strategy, architecture, and delivery of a core capability within Enterprise Data Platform in Aladdin Data. This role combines platform engineering, data governance, and stakeholder leadership to build a scalable, trusted, and transparent framework for data quality across the firm. The platform ensures that the data BlackRock relies on is fit for purpose across key dimensions including completeness, accuracy, timeliness, consistency, validity, and integrity. It provides clear and actionable quality signals to upstream producers, downstream systems, and end users so data can be used confidently for decisions at scale. The framework uses custom Python operators, Great Expectations, and Airflow-orchestrated pipelines to perform quality checks as data moves through the ecosystem. The ideal candidate brings strong technical depth, sound architectural judgment, hands-on execution, and the ability to align stakeholders around a common platform vision. What You Will Do Lead the evolution of BlackRock’s data quality framework as a strategic platform capability for validating and monitoring data across the Aladdin Data ecosystem. Define the technical direction for a metadata-driven framework that supports reusable quality rules, policy enforcement, exception handling, quality scoring, and domain-level service standards. Design and deliver controls that run within Airflow-orchestrated pipelines, enabling early detection of issues before they affect downstream systems or clients. Build a strong operating model for observability, transparency, and remediation so producers and consumers can identify and resolve issues quickly. Partner with engineering, product, governance, and business stakeholders to drive adoption, prioritization, and long-term roadmap execution. Key Responsibilities Own the target-state architecture for the Data Quality Framework, including rule execution patterns, validation layers, quality gates, exception workflows, and extensibility standards. Build and scale platform services, libraries, and APIs for rule authoring, execution, scoring, auditability, and quality SLA and SLO reporting across datasets and domains. Develop controls across core quality dimensions, including completeness, accuracy, timeliness, consistency, validity, uniqueness, and referential integrity. Design and implement profiling, anomaly detection, and drift detection capabilities covering schema changes, null patterns, distribution shifts, outliers, volume trends, and freshness checks. Implement reconciliation and financial control patterns such as source-to-target checks, row-count balancing, aggregate validation, hashes, and critical total checks. Drive adoption of Great Expectations and custom Python operators to standardize how assertions are defined, executed, versioned, and reused across pipelines. Integrate the framework into Airflow-based data pipelines so checks run at the right control points with meaningful alerting and triage. Establish metadata-driven rule management, including ownership, lineage, versioning, parameterization, execution history, and audit-ready evidence. Optimize framework performance across high-volume environments, particularly Snowflake and MSSQL, balancing control rigor with runtime efficiency. Create clear visibility for downstream platforms, internal users, and clients through dashboards, scorecards, status indicators, and actionable exception reporting. Mentor engineers and act as a senior technical leader who can make pragmatic architecture decisions while staying hands-on when needed. Influence enterprise standards for trusted data consumption in partnership with data governance, platform engineering, and product teams. Required Qualifications At least 10 years of experience in backend, data platform, or data engineering roles, with a strong record of hands-on technical delivery. Deep expertise in Python and ... (truncated, view full listing at source)