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Associate, Snowflake Data Engineer – BTO

BlackRock
2 LocationsPosted 14 May 2026

Job Description

About this role Associate, Snowflake Data Engineer – BlackRock Transformation Office About BlackRock Transformation Office (BTO) BlackRock Transformation Office (BTO) is a global group of change professionals that deliver strategic transformation to help make BlackRock greater than the sum of its parts.  We drive 1BLK outcomes by continuously connecting, and being students of, the firm’s people, processes, and technology. We are a versatile team made up of individuals with varied experiences and backgrounds motivated by the challenge of finding solutions which benefit BlackRock and our clients. In BTO, we pride ourselves on developing our people, improving leadership and delivery skills, and broadening firm and industry knowledge.  Projects and roles are assigned not just based on successful delivery, but also on individual development objectives and career aspirations. Job Description The Snowflake Developer will be responsible for designing, developing, and maintaining Snowflake environments that support data storage, processing and analytics for BlackRock’s corporate technology data platforms.  This role plays a critical part in building and managing scalable data architectures, robust data models and automated data pipelines that enable efficient and reliable integration, transformation and delivery of data.  The developer will work closely with cross-functional teams to translate business initiatives into technical solutions, ensuring data quality, integrity and performance across BlackRock systems. Key Responsibilities Snowflake Administration & Maintenance Manage and maintain Snowflake databases, including performance monitoring, cost optimization, troubleshooting and backups Partner with BlackRock’s Snowflake infrastructure team to implement best practices, coordinate upgrades and integrate enterprise-wide features Ensure compliance with data governance, security and audit requirements Data Engineering & Pipelines Design and develop automated data pipelines using Fivetran and other ETL tools, integrating data from various business systems, BlackRock warehouses, APIs and external sources Implement robust data validation, cleaning and transformation processes to ensure data quality, accuracy and reliability Monitor and tune data pipelines for performance, scalability and cost efficiency Data Modelling & Architecture Design and implement logical and physical data models to support BlackRock stakeholders’ analytical and reporting needs Create ER diagrams, data flow diagrams and other visualizations to represent data models and workflows Develop and enforce data modeling best practices and standards Optimize data models for query performance, ensuring efficient data retrieval across large datasets Collaboration & Support Partner with BlackRock business and corporate technology teams to translate business requirements into technical solutions Train and support end users on Snowflake capabilities, data access and reporting tools Collaborate with vendors and cross-functional teams to troubleshoot issues, deliver enhancements and implement new features Job Requirements Education Bachelor’s degree in computer science, data science, software engineering, information systems or related quantitative field (master’s degree preferred) Experience Minimum of 3-5 years’ experience in data engineering, including data integration, modeling, optimization and data quality Proven experience designing, developing and maintaining data warehouses using Snowflake in a production environment Experience working in financial services or private markets (preferred) Skills Strong SQL skills for querying, data manipulation and performance optimization Proficiency with modern ETL/ELT tools ( e.g. , Fivetran, dbt, ADF) Expertise in data modeling concepts (relational, dimensional and star schema) Proficiency in Python, Java or Scala for data engineering tasks Knowledge of cloud services (AWS, Azure GCP) and data inte ... (truncated, view full listing at source)
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