Local to Tampa Bay Area for hybrid onsite model at St Petersburg, FL
No C2C, On W2 only
Knowledge of:
Foundational data quality principles and how they contribute to ‘fit for purpose’ data. · Data development lifecycle – requirements, source analysis, data design, ETL development, test & validation, deployment, monitoring & operations, governance & stewardship · Concepts, principles and practices of internal controls and data management and governance. |
· Principles of banking, finance and securities industry operations, and familiarity with accounting and finance related topics. |
· General familiarity with risk and control framework of Category IV financial institution. |
· Concepts of business analytics and familiarity with business intelligence tools such as Python, Tableau, Qlik, MS Power BI, MS Teams, SharePoint, etc. · Experience in using data gap remediation and data quality monitoring tools (particularly Microsoft DevOps (TFS), Ab Initio, Collibra, IBM InfoSphere or Informatica Data Quality) is a plus. · Familiarity with Federal Reserve Bank (FRB) and/or Office of the Comptroller of the Currency (OCC) regulatory requirements or other regulatory bodies is a plus. |
Skill in:
· Hands-on expertise in leveraging Domain Specific Languages such as SQL to retrieve, combine and manipulate large data sets.
· Deep understanding of enterprise data management concepts, principles and current industry practices.
· Strong “soft” skills in collaboration, facilitation, coordination, logistics, presentation, marketing, and excellent communication skills (both written and verbal).
· Experience delivering data quality improvements in a complex, transforming, multi-national banking environment.
· Organizational and planning skills, showing an ability to operate in a multi-geography, matrix organization; managing and where necessary leading activities through cross-functional teams, with the discipline to track delivery of complex deliverables over multiple months.
· Excellent communication skills (both written and verbal), including the ability to present complex concepts and strategies successfully.
· Demonstrated strong execution capabilities and entrepreneurial mindset to go above and beyond delivering core responsibilities.
Ability to
· Analyze, problem-solve and prioritize
· Demonstrate strong analytical thinking and autonomy in translating problems into efficient, executable solution
· Understand data models and taxonomies, with deep skills in data quality, controls and remediation
· Produce and articulate meaningful data quality metrics and KPIs
· Help drive data quality strategy and foster data culture across the organization
Never miss an opportunity! Create an alert based on the job you applied for.
