Chief Data Office - Data Quality - Business Analytics Associate

Job Description — Senior Associate, Business Analytics (Chief Data Office, CIB) Role Summary The Chief Data Office (CDO) within the JPMorganChase Commercial & Investment Bank (CIB) is seeking a Senior Associate to support high-priority CIB initiatives focused on increasing quality of Sales’ data for the Markets business. This role is project-driven (little fixed BAU), requires strong analytical problem-solving, and will partner closely with business, product, and technology stakeholders to identify data quality issues, size impact, and drive resolution paths from discovery through remediation. Key Responsibilities Deliver analytics for high-priority CIB programs , per the Sales CDO’s strategy, with a focus on improving the quality and usability of data used by Markets Sales. Perform data quality assessments (profiling, anomaly detection, reconciliation, completeness/accuracy/timeliness checks) and quantify business impact. Lead issue triage and resolution planning : document findings, identify root causes, propose remediation options, and define/track resolution paths with owners. Build and maintain repeatable workflows for data preparation and analysis. Develop dashboards and reporting to monitor data quality metrics, remediation progress, and key outcomes for senior stakeholders. Partner cross-functionally with CIB stakeholders (product teams, data owners, tech teams, control/operational partners as applicable) to align on definitions, requirements, and prioritization. Improve documentation and controls : data dictionaries, business rules, issue logs, and operational runbooks for project deliverables. Required Qualifications Bachelor’s degree or equivalent practical experience. Strong experience in business analytics / data analytics roles (typically 5+ years, or equivalent depth). Hands-on capability with: Alteryx (automating analytics workflows, data blending, validation) Dashboarding (e.g., Tableau, Power BI, or similar—building stakeholder-ready views and KPIs) SQL / Python (data extraction, cleaning, analysis, automation; ability to productionize analytics where appropriate) AI-enabled improvement experience demonstrated ability to apply AI/ML and generative AI techniques to reduce manual effort, accelerate data analysis, and improve decision making Demonstrated ability to work in ambiguous, project-based environments with shifting priorities and tight timelines. Strong stakeholder management and communication skills, including writing clear issue narratives and presenting insights to non-technical audiences. Proven experience identifying data quality issues and driving structured remediation with accountable owners. Preferred / Nice-to-Have Experience with data quality tooling (e.g., Informatica, Collibra or similar DQ/profiling platforms). Familiarity with data governance concepts (critical data elements, lineage, controls, metadata, stewardship). Experience working with Client/Sales/CRM-type datasets (contacts, coverage, relationship hierarchies, Core Reference data) and associated quality challenges. Comfort working across large enterprise data ecosystems and collaborating with data and engineering teams. Competencies Analytical rigor and curiosity; strong hypothesis-driven problem solving Strong attention to detail; comfort with imperfect data Ability to translate business problems into data tests and measurable metrics Self-starter, able to work autonomously, with strong time management skills; efficient at multi-tasking and able to work under pressure to deliver multiple business demands on-time, to a high standard Excellent communication, presentation (both oral and written) & influencing skills - candidate will be dealing with stakeholders of varying degrees of seniority