Data Engineer Overview • Lead data engineering efforts to design and implement scalable, high-performance solutions supporting organizational goals. • Collaborate with cross-functional teams to drive innovation in data platform technologies and workflows. • Utilize advanced tools and methodologies to address complex data challenges and optimize systems. • Enhance your technical expertise in database technologies, automation, and infrastructure optimization. • Contribute to mission-critical systems supporting diverse investment strategies and decision-making processes. • Engage in a fast-paced environment with ample opportunities for professional growth and skill development. • Be part of a collaborative team focused on achieving excellence in data engineering and innovation. • Impact the financial sector by enabling data-driven strategies and operational efficiency. Data Engineer Key Responsibilities & Duties • Design, implement, and maintain robust database systems ensuring reliability and scalability. • Develop and optimize automation scripts using SQL, PowerShell, and Python for streamlined workflows. • Collaborate on database design, modeling, and schema versioning to achieve optimal performance. • Implement CI/CD pipelines using industry-standard tools to enhance deployment processes. • Monitor and improve system performance using advanced monitoring tools and techniques. • Troubleshoot and resolve database issues to ensure operational continuity and reliability. • Stay informed on emerging technologies and integrate them into existing systems effectively. • Collaborate with stakeholders to align data solutions with organizational objectives and goals. Data Engineer Job Requirements • Bachelor of Science (BS) degree in a relevant field is required. • Minimum of 3 years of experience in data engineering, preferably within the financial sector. • Proficiency in Microsoft SQL Server and scripting languages such as SQL, PowerShell, and Python. • Experience with Infrastructure as Code (IaC) and CI/CD pipeline tools is essential. • Knowledge of database performance tuning and optimization techniques is required. • Familiarity with monitoring tools and distributed data platforms is preferred. • Strong communication skills for effective collaboration and documentation are necessary. • Preferred skills include exposure to Apache Iceberg, PostgreSQL, and Kubernetes.