Additional Information
About this role
Business Overview
PMGTech is the horizontal technology platform within BlackRock's Portfolio Management Group (PMG), integrating investment research with advanced engineering, AI, and alternative data to make technology a direct driver of alpha. It operates as one global team across three verticals - Platform Strategy, Research Solutions (DS&S), and Platform Change (IPT) - partnering closely with the BlackRock Aladdin ecosystem and PMG investment leadership. This integrated model delivers a coherent tech strategy, strengthens the research community, and scales capabilities across regions, asset classes (Equities, Fixed Income, Multi-asset), and investment styles (discretionary and systematic).
PMGTech leads PMG's AI strategy by building an AI-ready research environment, unified data layer, agentic orchestration networks, and an investment-focused application layer to deploy AI-powered research solutions into production. As part of PMGTech , you will work directly with investment researchers and portfolio managers to build and scale capabilities in data engineering, GenAI, and platform tooling that streamlines research workflows and enables differentiated alpha-generative investment insights across PMG's investment pillars.
Job Purpose / Background
The Research Engineer supports investors and research teams by building data workflows, research tools, analytics pipelines, and AI-assisted capabilities that help transform investor hypotheses into actionable insights. Embedded within DS&S research pods - asset-class focused sub-teams - this role requires collaboration with investors/researcher s, AI Leads, and core engineering team to deliver high-quality data assets, exploratory analyses and research-enabling components.
This role requires that the engineer understand the business context and hypothesis of investors and translate the requirement into functional data solutions. This requires expertise in building and operating data intensive research solutions using Python and SQL, workflow orchestration, Cloud Big Data technologies such as BigQuery / Snowflake / GCS , visualization tools such as Tableau/Power BI and experience in data quality, CI/CD pipelines, and data operations.