Data Engineer, Associate

About this role Job Background The Analytics and Automation team within the EMEA Core COO organisation leverages technology, data, and AI to deliver management information and analytics that drive actionable insights into sales performance and client engagement across the EMEA client businesses. The team plays a critical role in shaping how BlackRock sells to and services its clients, enabling better decision‑making through the effective use of data. The team partners closely with Technology and Engineering teams to design and deliver high‑impact data and visualisation tools for COO and Distribution stakeholders. You will also collaborate with internal technology teams on infrastructure, tools, processes, standards, and development practices, as well as work alongside data science and analytics teams across the firm. The successful candidate will bring a strong passion for technology, data, and client outcomes, with comfort working across a broad range of technical capabilities, including databases, software development, and cloud infrastructure. This role suits someone who enjoys solving complex problems and building scalable, high‑impact data products. At BlackRock, we value curiosity, continuous learning, and professional growth. With over $14 trillion in assets under management, we have a unique responsibility: our products and technology empower millions of investors to save for retirement, pay for education, purchase homes, and improve their long‑term financial wellbeing. Key Responsibilities Explore, profile, cleanse, and preprocess data to ensure high‑quality datasets for analytics, reporting, and downstream consumption. Design and manage workflows for storing and retrieving vectorised documents to support AI‑enabled use cases. Apply embedding models to build AI‑driven solutions. Leverage modern AI and machine‑learning techniques, including large language models (LLMs) and agent‑based systems, to enhance data workflows and automation. Design, build, and maintain scalable ELT pipelines in Snowflake, covering data ingestion, transformation, and publication layers for enterprise use. Develop and optimise Snowflake data models (schemas, views, and curated datasets) to enable consistent, performant, and well‑governed access. Implement robust data quality controls, including validation, reconciliation, monitoring, and alerting, to ensure the accuracy and reliability of critical datasets. Partner with central platform and data engineering teams to support Snowflake architecture, including performance tuning, warehouse optimisation, security patterns, and cost‑effective usage. Write high‑quality, maintainable code that is well tested, documented, and aligned with engineering best practices, including version control and peer review. Build and maintain Streamlit applications to enable self‑service data exploration, operational tooling, and lightweight analytics for business users, including applications that interact directly with Snowflake datasets and stored procedures. Translate business questions into technical solutions, delivering clear outputs and actionable insights for both technical and non‑technical stakeholders. Skills and Competencies Strong experience with Snowflake and advanced SQL, including query optimisation and best‑practice analytical data modelling. Knowledge of modern AI and machine‑learning techniques, including large language models (LLMs) and agent‑based systems, embedding modes and document vectorization Experience developing and maintaining data transformation workflows using dbt within Snowflake, including modular modelling, testing, and documentation. Proficiency in Python for data engineering and application development, including data processing, orchestration patterns, and reusable components. Experience building Streamlit applications, ideally in an enterprise environment, with a focus on usability and integration with Snowflake‑backed data products. Familiarity with mode ... (truncated, view full listing at source)