A leading global investment manager is expanding its data engineering function to support investment, research, and operational teams across the firm. The data team is responsible for building and maintaining the core data platform that powers analytics, reporting, and critical investment decision-making. The firm is seeking a Data Engineer to design and build modern data pipelines, contribute to the ongoing evolution of its cloud-based platform, and deliver reliable, scalable, and well-modeled data assets to consumers.
Key Responsibilities
- Pipeline Design & Maintenance: Design, build, and maintain scalable data pipelines using Databricks and Apache Spark to process and transform large, complex datasets.
- Stakeholder Partnership: Partner with data consumers and technology teams to understand business requirements and deliver well-modeled, production-grade data assets for various downstream use cases.
- ETL/ELT Optimization: Develop and optimize ETL/ELT workflows with a primary focus on performance, reliability, data quality, and long-term maintainability.
- Platform Contribution: Contribute to the ongoing development of the firm's cloud data platform, encompassing internal tooling, automation, and high engineering standards.
- Monitoring & Validation: Implement comprehensive monitoring, testing, and validation protocols to ensure the ongoing accuracy and integrity of data flowing through the platform.
Requirements & Qualifications
- Education: Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent professional experience.
- Experience: 2+ years of professional data engineering experience building and maintaining production-grade data pipelines.
- Core Technical Skills:
- Hands-on experience with Databricks and Apache Spark .
- Strong programming proficiency in Python and SQL .
- Solid conceptual understanding of data modeling, distributed data processing, and modern data engineering practices.
- Cloud & Scale: Demonstrated experience working with cloud data platforms and large-scale data processing workloads.
- Soft Skills: Strong communication skills with a proven ability to collaborate effectively with both technical and non-technical stakeholders.
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