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# Strategic Data Provisioning Specialist / Lead at Chief Data & Analytics Office (CDAO) - Assoc / VP

- **Company:** Jpmorganchase
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Profiling, Data Architecture, Information Engineering, Data Governance, Data Profiling, Executive Information Systems, Github, R (Programming Language), Revision Control Systems, Graph Database, Python (Programming Language), Metadata, Meta-Data Management, Power BI, SQL Databases, Tableau (Software), Apache Spark, Git, Data Lineage, Data Analytics, Bitbucket, Data Management
- **Published:** August 29, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/strategic-data-provisioning-specialist-lead-at-chief-data-analytics-office-cdao-assoc-vp/45324669

## About the Role

friction caused by incomplete documentation or poor catalogue qualityRequired qualifications, capabilities, and skillsFormal training or certification on data engineering concepts and advanced applied experienceDeep subject matter expertise in wealth and asset management data domains, including customer, account, position, transaction, and/or reference dataProven ability to deliver results in a matrixed and complex environment, with demonstrated experience influencing stakeholders at all levels of an organizationExperience leading strategic or transformational data initiatives, including data governance, data quality programs, or analytics transformationStrong technical proficiency in data profiling, analysis, and data management using modern tools and environments, including Python, R, SQL, Spark, and cloud platformsExperience with data quality frameworks, including profiling, rule development, issue remediation, and the design of preventative controlsAbility to translate
complex technical findings into clear executive-level communications, dashboards, and performance reportingPreferred qualifications, capabilities, and skillsHands-on experience with data lineage tools and techniques, including graph databases and metadata management platformsExperience with data visualization and reporting tools such as Tableau or Power BI to deliver executive dashboards and performance metricsKnowledge of data governance frameworks, data quality dimensions, and regulatory requirements such as BCBS 239 or GDPRExperience applying AI and machine learning techniques to data management challenges, such as automated data profiling or metadata enrichmentFamiliarity with agile and product management methodologies, with experience working in cross-functional agile teamsExperience with version control tools such as Git, GitHub, or Bitbucket in a collaborative engineering environment #J-18808-Ljbffr

## Description

cross-product dependencies, and demonstrating value at every stage of the data lifecycle. You will bring deep technical expertise and strategic thinking to solve complex data challenges, while collaborating with senior stakeholders to deliver measurable results.Job responsibilitiesProvision new and differentiated data assets to support AI and analytics initiatives, partnering with use case owners to define requirements, manage product dependencies, and accelerate delivery through AI-enabled data toolingDrive executive visibility into the progress of making critical data sources available, including performance metrics, adoption tracking, and transparency into bottlenecksSupport agile product routines to oversee cross-product data dependencies, prioritize delivery, and align resources across business, technology, and operations partnersIdentify and document the lineage and provenance of critical data assets to support governance, regulatory, and business requirements, embedding evergreen
controls to improve safety and traceabilityDevelop and deliver data lineage analysis and reporting that provides executive visibility on progress against critical service-level agreements, including blockers and resourcing needsLead root cause analysis of data quality issues using deep data profiling and advanced analytics techniques, fixing identified issues and embedding preventative controls to reduce future failuresDrive operational efficiency by eliminating the cost of poor data quality through common tooling, frameworks, and proactive control developmentEnrich the metadata and semantic layer of existing data assets to improve discoverability, usability, and value for AI applications and natural language query capabilitiesAccelerate adoption of mesh data architecture by uplifting existing data assets with improved metadata, data quality scores, and lineage informationDevelop and deliver data product prototypes that demonstrate the value of enriched data assets and reduce consumer


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