Trade Attribution & CBOR Analyst: Data Quality & Automation

Aumni; Ohio; On-site; USD 70,000 - 100,000; Full time

Job summary

JPMorgan Chase & Co. in Columbus, OH is seeking a Trade Attribution and Support Analyst to own daily attribution of trades and client accounts across asset classes, ensuring data integrity in the CBOR.

You will investigate exceptions and provide clear, data-backed explanations with end-to-end traceability. You will partner with Trading, Middle Office, Operations, and Technology to capture required trade attributes, run controls, and produce standard reporting on attribution rates, breaks, and

Qualifications

• Bachelor’s degree or equivalent in Finance, Business, Economics, Information Systems, Statistics, or related discipline.
• 1–4+ years of experience in asset management operations, middle office, trade support, trade processing, client reporting operations, or a comparable role working with trade-level data.
• Solid understanding of the trade lifecycle and operational nuances across multiple asset classes (trade capture, allocations, confirmations, settlement concepts, lifecycle events).
• Demonstrated ability to work with large datasets, reconcile differences, and deliver accurate results under time constraints.
• Advanced Excel skills (data cleaning, pivots, lookups; comfort handling large files and multiple sources).
• Strong written/verbal communication skills and ability to support a client-facing book of business through Sales/Business Management partners.
• Strong control mindset: accuracy, documentation, repeatability, and operational risk awareness.

Responsibilities

• Perform daily trade attribution/matching of executions to client accounts, allocations, and orders across all asset classes and trade types.
• Monitor and resolve exceptions impacting trade attribution and CBOR integrity (e.g., missing/incorrect identifiers, allocation breaks, booking issues, lifecycle event impacts, timing discrepancies).
• Partner with trading, middle office, operations, and technology teams to ensure accurate, timely capture of trade attributes required for CBOR (e.g., account mappings, instrument identifiers, trade dates, prices, fees, commissions, FX rates).
• Support the Client Book of Record (CBOR) by validating trade-level data quality, completeness, and consistency across upstream/downstream sources (trade capture → allocation → settlement → reporting).
• Execute daily controls and reconciliations to identify breaks, aging, and root causes; track remediation and ensure issues are resolved within agreed SLAs.
• Maintain documentation of controls, procedures, and data lineage to support auditability and operational risk standards.
• Research and respond to trade-related questions from Business Management and Sales.
• Provide concise, business-friendly explanations supported by underlying data and clear traceability through the trade lifecycle.
• Develop standardized reporting packs and response templates to improve consistency and turnaround time.
• Produce recurring and ad hoc reporting on attribution rates, break volumes, aging, resolution effectiveness, and CBOR quality metrics.
• Identify recurring issues and propose process improvements, automation opportunities, and data-quality enhancements.
• Collaborate with technology/data partners to refine data pipelines, exception dashboards, and control frameworks.

Skills

Advanced Excel

Client-facing communication

Trade lifecycle knowledge

Data reconciliation

Education

Bachelor’s degree or equivalent in Finance/Business/IS

Tools

SQL

Alteryx

Python (pandas)

Cognos

Job description

JPMorgan Chase & Co. in Columbus, OH is seeking a Trade Attribution and Support Analyst to own daily attribution of trades and client accounts across asset classes, ensuring data integrity in the CBOR.

You will investigate exceptions and provide clear, data-backed explanations with end-to-end traceability. You will partner with Trading, Middle Office, Operations, and Technology to capture required trade attributes, run controls, and produce standard reporting on attribution rates, breaks, and