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About this Role: At BlackRock, technology is the foundation of our business. As a Data Engineer, you’ll build resilient systems that power our global post-trade operations. You’ll design 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 fostering a culture of excellence and continuous improvement.
Job Responsibility
Partner with domain experts, product, and engineering teams to design 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
Design 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 best practices (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 best practices for documentation, lineage, schema evolution, and secure handling of sensitive data (PII) in collaboration with platform and governance partners
Requirements
B.S./M.S. in Computer Science, Engineering, or related discipline (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 designing and implementing 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 comfort collaborating with domain experts to turn 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., Kafka, CDC patterns) and understanding of late-arriving data, watermarking, and replay
Experience integrating downstream serving/search systems (e.g., Elasticsearch) and operational datastores (e.g., Cosmos DB)
Familiarity with data governance and observability tooling (catalog/lineage, OpenLineage-style concepts, data quality frameworks)
Cloud-native exposure (Docker/Kubernetes, AWS/Azure/GCP) and infrastructure-as-code (Terraform)
Interest in financial systems, accounting, or investment technology