Enterprise Data Engineer - Trading Systems
Position Overview
Responsibilities
Design, develop, test, and support Python and SQL-based data solutions.
Build and maintain data pipelines, integrations, and enterprise data warehouse platforms.
Support and integrate with enterprise and vendor platforms including BlackRock Aladdin, Eagle PACE, NeoXam DataHub, and similar investment management and data management systems.
Develop scalable data models and optimize data access patterns for performance, reliability, and maintainability.
Enhance data platforms through new applications, interfaces, automation, and reporting capabilities.
Troubleshoot and resolve complex production issues across data and integration workflows.
Participate in architecture discussions, code reviews, and engineering best practices.
Write clean, maintainable, and well-documented code.
Collaborate with technical and business teams in an Agile environment.
Qualifications
5+ years of Data Engineering or Software Engineering experience.
Strong hands-on experience with Python and SQL.
Expertise in relational databases, data modeling, query optimization, indexing, and ETL development.
Experience building and supporting production-grade data platforms and data warehouses.
Cloud experience, preferably with Azure technologies such as Azure SQL, Azure Data Factory, and Azure Data Lake.
Experience integrating with investment management, data management, accounting, or reporting platforms such as Aladdin, Eagle, NeoXam, or similar enterprise systems.
Understanding of software engineering best practices including testing, version control, and code reviews.
Strong analytical and problem-solving skills.
Experience within financial services, asset management, capital markets, or other data-intensive industries preferred.
Exposure to AI-assisted development tools and the ability to independently validate and improve generated code.
Passion for continuous learning, technical excellence, and mentoring others.
What You'll Gain
Work on high-impact enterprise data platforms supporting critical business functions.
Exposure to investment, operations, data management, and reporting workflows.
Opportunity to solve complex data engineering challenges at scale.
Collaborative engineering culture focused on quality, innovation, and continuous improvement.
FAQs
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