Vice President, Data Quality Lead Engineer

About this roleBlackRock 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 Directed acyclic graph-based workflow orchestration framework for data and batch processing-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 Directed acyclic graph-based workflow orchestration framework for data and batch processing-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 Directed acyclic graph-based workflow orchestration framework for data and batch processing-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 experience building reusable engineering frameworks, services, or platform capabilities. Strong experience with workflow orchestration and pipeline integration, ideally with Directed acyclic graph-based workflow orchestration framework for data and batch processing in complex enterprise environments. Proven experience designing and implementing data quality controls across batch and or near-real-time data pipelines. Strong understanding of enterprise data quality operating models, including SLAs and SLOs, exception handling, issue triage, and remediation workflows. Hands-on experience with Great Expectations or similar data quality frameworks, with the ability to extend them through custom engineering patterns. Strong proficiency with Snowflake and or MSSQL, including query tuning, scalable control execution, and performance optimization. Experience with metadata-driven platform design, data modeling, lineage, traceability, and audit-ready control frameworks. Demonstrated ability to make architecture decisions, influence platform direction, and communicate effectively with senior technical and business stakeholders. Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience. Preferred Qualifications Experience building enterprise data quality, observability, or control frameworks in financial services or similarly regulated environments. Exposure to FastAPI or similar API frameworks for building platform services and developer-facing capabilities. Experience with event-driven or streaming architectures such as distributed event streaming and messaging platforms for near-real-time quality detection patterns. Familiarity with Open Container Initiative (OCI) container image packagi