
IT Senior Data Quality Analyst
Brooklyn, OHJobSeen 3 days agoSeen in employer's feed 3 days ago
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Job overview
KeyBank’s Enterprise Data Quality team seeks a senior analyst to lead daily operations for its data quality rule-writing Coverage team. The role develops and assures rules, supports partners creating rules, and advances data quality coverage across the bank. The analyst coordinates workstreams, mentors colleagues, and advises stakeholders, using banking knowledge, data quality practices, and advanced SQL to help ensure data is trustworthy and fit for use.
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Full job description
Location: 4910 Tiedeman Road, Brooklyn Ohio Job Summary The Enterprise Data Quality team at KeyBank is seeking a highly motivated and detail-oriented Data Quality Senior Analyst to lead the daily operations within the data quality rule writing Coverage team. The Coverage team ensures data assets at Key are supported by the appropriate data quality rules, through direct rule definition & implementation, as well as supporting partners throughout Key as they create and implement their own rules. This is a foundational component of our Enterprise Data Quality Program. This role is essential to ensuring data is trustworthy and fit for its intended use across regulatory, analytics, AI, and reporting functions. The ideal candidate is a passionate data advocate with a strong understanding of banking processes, data quality principles, tools, and best practices paired with advanced SQL proficiency that is critical to effectively interrogating, validating, and monitoring data at scale. The successful candidate will demonstrate the ability to lead through influence, drive team execution, and coordinate complex workstreams across stakeholders while operating as a senior individual contributor. Leveraging technical expertise, business acumen, and leadership capabilities, the Senior Analyst will drive data quality coverage efforts by leading internal quality development and assurance processes, organizing and prioritizing work within the team, mentoring colleagues, and serving as a trusted advisor for DQ rules created within the team and by our partners across the Bank. Responsibilities - Lead the daily operations within the Coverage team. - Drive organization & orchestration of tasks within the Coverage team - Take ownership & help drive the Enterprise Data Quality Coverage strategy. - Serve as the senior technical expert for the design, implementation, and ongoing management of DQL-based data quality rules aligned to both enterprise standards and business objectives. - Collaborate with data owners and stakeholders to define and implement SQL based rules in the Key DQ application. - Apply business acumen, data profiling, and analysis techniques to enhance quality rule design. - Lead internal code (quality rule) review & quality assurance processes. - Facilitate interactive sessions with stakeholders to review and enhance rule results. - Build strong relationships with data owners, custodians, and consumers to increase data quality support for existing processes. - Look for continual improvement in the data quality measurement process. Required Skills - Strong attention to detail and comfort with data - Proven organizational and project management skills - Excellent communication skills - Ability to form relationships and collaborate effectively with data owners - Problem-solving skills and ability to support and escalate issues as needed - Strong SQL skills - Experienced partnering with both technical and non-technical roles Additional Skills - Experience with data quality management and defect tracking systems - Analytical skills to understand and interpret data issues - Ability to work independently and manage multiple tasks simultaneously - Proficiency in data analysis tools and software - Strong ability to prioritize tasks effectively - Continuous improvement mindset to enhance the defect management process Required Qualifications 5 + years of experience, preferably in Banking or Finance 2+ years of experience in data quality, ideally establishing, operationalizing, and continuously improving aspects of a data quality program and practices (e.g., DCAM) Understandin
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