Data Analyst – Mid/Back Office Tech Team
$80,000–$100,000 year
On-siteChicago, Illinois, United States
Chicago, Illinois, United StatesOn-siteFull Time$80,000–$100,000 yearFinanceSmall
Full TimeSmallFinance
Job Summary
Design Qlik Sense dashboards, develop data load transformations, and manage audit trail reporting for accounting and compliance groups. Build and optimize backend services supporting the full trade lifecycle, ensuring accurate data flow from front-office systems to middle and back-office platforms. Resolve time-sensitive production issues and improve system performance through monitoring and automation. Maintain daily reporting to third parties with high accuracy and quality.
Required Qualifications
- Academic degree in Computer Science or another STEM-related field of study
- 2+ years of experience as a Data Analyst or Data Engineer
- Strong programming and data-querying skills, with strong expertise in advanced SQL and one or more general-purpose programming languages
- Strong understanding of relational databases, data modeling, data quality, and performance optimization
- Experience developing business intelligence dashboards, operational reports, and self-service analytics solutions using a modern visualization platform
- Proven familiarity with the end-to-end software development lifecycle from version control and automated testing to containerized deployments, system monitoring, and ongoing production support
- Excellent communication skills and the ability to operate effectively in a fast-paced trading environment
- Self-starter with a proven ability to work well independently and in a team environment
- Ability to plan and prioritize time to meet strict deadlines
Desired Qualifications
- Experience supporting systems or data processes across a complete operational lifecycle, such as trade capture, reconciliation, profit and loss, settlement, accounting, or regulatory reporting
- Experience in trading, financial services, regulated industries, or another environment requiring high accuracy, reliability, and auditability
- Exposure to artificial intelligence or machine learning, particularly for workflow automation, anomaly detection, data-quality monitoring, or intelligent reporting
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