Data Engineer, Aladdin Engineering, Vice President

About this role About this Role At BlackRock, technology is the foundation of our business. As a VP, Data Engineer, you’ll build resilient systems that power our global post-trade operations. You’ll develop and deliver enterprise-scale software with a focus on reliability, performance, and clean engineering practices. This role is ideal for engineers who like to innovate and solve complex challenges while encouraging a culture of excellence and continuous improvement. About Post Trade Accounting (PTA or equivalent experience) A major strategic area within Aladdin and one of BlackRock’s largest engineering investments. Responsible for the systems that ensure accurate , scalable, and efficient accounting across global operations. Growing into data analytics and pipeline initiatives using Snowflake, Redis, and Kafka to manage high-volume, real-time data. Works in close partnership with Product, Operations, and other Engineering teams to provide business-critical capabilities. Agile and collaborative environment that values technical depth, quality, and innovation. Key Responsibilities Partner with domain experts, product, and engineering teams to build canonical data models (conceptual → logical → physical) that power trusted reporting, analytics, and downstream integrations. Build and evolve analytics-ready datasets in Snowflake (curated layers / data marts), including clear metric definitions (grain, dimensions, measures) that enable consistent enterprise reporting. Build and develop reliable ELT/ETL pipelines across Snowflake and SQL Server to support both scheduled batch loads and low-latency ingestion where needed. Implement robust pipeline patterns such as incremental processing, idempotency (replay-safe loads), deduplication, and backfill/reprocessing strategies. Establish and enforce data quality and observability practices (freshness, completeness, accuracy checks; alerting; runbooks; SLAs) to keep data products production-grade. Optimize analytical performance and cost by applying Snowflake guidelines (clustering/partition strategies, materializations, query optimization) and SQL Server performance tuning where appropriate . Publish curated data to downstream systems and serving layers when needed (e.g., search indices like Elasticsearch and operational stores like Cosmos DB) with clear contracts and monitoring. Drive guidelines for documentation, lineage, schema evolution, and secure handling of sensitive data (PII) in collaboration with platform and governance partners. Qualifications / Proficiencies B.S./M.S. in Computer Science, Engineering, or related field (or equivalent practical experience). 8 years of experience building production data systems, with demonstrated ownership of data modeling and data pipeline engineering. Strong SQL skills (advanced querying, query plans, performance tuning) with hands-on experience in Snowflake and/or Microsoft SQL Server. Proven experience with data modeling for analytics (dimensional modeling / star schemas, conformed dimensions, slowly changing dimensions) and translating business concepts into robust schemas. Hands-on experience crafting and deploying ELT/ETL pipelines, including batch and near-real-time patterns. Proficiency in at least one general-purpose language used for data engineering (e.g., Python, Java, or Scala) for automation, orchestration, and integrations. Working knowledge of modern data engineering practices: testing for transformations, CI/CD, environment promotion, and operational monitoring. Strong communication skills and ease working alongside domain experts to transform ambiguity into clear, implementable data products. Nice to Have Experience with transformation and modeling frameworks (e.g., dbt ) and/or a semantic/metrics layer approach. Exposure to orchestration tools (e.g., Airflow, Dagster , Prefect) and patterns for dependency management and backfills. Streaming and event-driven data experience (e.g ... (truncated, view full listing at source)