Associate Data Engineer, Data Management – Data Office

BlackRock•New York, NY

11d•Hybrid

About The Position

The BlackRock Data Office (BDO) builds and advances the firm's enterprise data capabilities through trusted, scalable, and governed data products that power investment, business, operational, and technology outcomes across BlackRock. As part of the Data Office organization, you will work closely with product managers, data stewards, platform engineers, software engineers, data scientists, and business stakeholders to deliver reusable data capabilities that support analytics, reporting, machine learning, artificial intelligence, and digital products. As a Data Engineer, you will play a key role in designing, building, and optimizing modern data platforms and pipelines that enable high-quality, reliable, and accessible data across the organization. Depending on experience and level, you will contribute to or lead the design and implementation of complex data engineering solutions, influence technical direction, and help establish engineering best practices across the organization. This role offers the opportunity to solve complex data challenges, build scalable distributed systems, and shape the next generation of enterprise data products and capabilities.

Requirements

Demonstrated ability to collaborate across global, cross-functional teams including data stewards, data scientists, platform engineers, and business stakeholders, and take ownership of major components of the data platform ecosystem.
Strong programming skills in Python, Java, and Scala, with experience working across large-scale distributed data analytics engines, cloud data platforms, Snowflake, and Structured Query Language (SQL).
Experience integrating and transforming data from flat-file sources such as comma-separated values (CSV), tab-separated values (TSV), Microsoft Excel, and database application programming interface (API) sources.
Typically 3–6 years of relevant experience in software engineering, data engineering, or related technical disciplines.
4+ years of strong Java, Python, or Scala programming experience, including hands-on experience developing user-defined functions (UDFs), reusable modules, and automated testing with frameworks such as pytest.
4+ years of experience building and optimizing large-scale data pipelines, architectures, and datasets. Familiarity with directed acyclic graph-based workflow orchestration frameworks for data and batch processing, dbt, and distributed event streaming and messaging platforms.
4+ years of hands-on experience developing production workloads using large-scale distributed data analytics engines, including resource allocation, performance tuning, and job optimization.
4+ years of experience using SQL-based analytics layers over large-scale data platforms, workload-monitoring tools, and data-optimization techniques including bucketing, partitioning, tuning, and schema-based data serialization and interchange formats.
4+ years of experience using Transact-SQL, relational databases, non-relational databases, and GraphQL.
Strong experience implementing solutions on Snowflake.
Experience with data-quality and validation frameworks, particularly Great Expectations.
Strong understanding of Swagger/OpenAPI for designing, documenting, and testing RESTful APIs.
Experience deploying and supporting solutions across cloud and hybrid environments, including Amazon Web Services (AWS), Microsoft Azure, OpenStack, Open Container Initiative (OCI) container image packaging and runtime, distributed event streaming and messaging platforms, and enterprise-grade container orchestration platforms supporting declarative infrastructure and horizontal scaling.
Familiarity with CI/CD pipelines for data-platform automation and deployment using tools such as Jenkins, GitLab CI, and Azure DevOps.
Experience with data governance, metadata management, and data lineage, including business glossaries, access controls, auditing, and centralized governance across cloud and hybrid environments.
Hands-on experience with Databricks, including notebooks, workflows, and machine learning integrations.
Experience working across global, cross-functional teams in a collaborative and fast-paced engineering environment.

Nice To Haves

Exposure to machine learning, artificial intelligence, generative AI, or engineering patterns that support AI-ready data platforms is beneficial.

Responsibilities

Design, develop, and maintain scalable, reliable, and high-performance data pipelines supporting enterprise data products.
Build and optimize batch and real-time data ingestion, transformation, and publishing processes across diverse data sources.
Develop reusable data engineering frameworks, components, and automation to improve platform efficiency and developer productivity.
Ensure data products meet enterprise standards for quality, security, governance, lineage, and observability.
Keep data separated and segregated according to relevant data policies.
Identify opportunities to improve performance, scalability, resiliency, and cost optimization across the data platform.
Troubleshoot complex production issues, perform root cause analysis, and implement sustainable long-term solutions.
Contribute to engineering standards, code reviews, testing practices, continuous integration and continuous delivery (CI/CD) automation, and technical documentation.
Automate manual ingestion processes and optimize data delivery subject to service-level agreements while partnering with infrastructure teams to improve scalability.
Stay current on emerging technologies and recommend innovative approaches that improve the firm’s data ecosystem.
Deliver high-quality engineering solutions while continuing to deepen technical expertise across modern data technologies.