Location: Bengaluru
Company: Wells Fargo
Employment Type: Full-Time
About the Role:
Wells Fargo is seeking a Senior Data Management Analyst to play a pivotal role in managing and governing enterprise data. This position involves ensuring data accuracy, integrity, and compliance while enabling secure and accessible data usage. You will work closely with both business and technical stakeholders to support large-scale data initiatives and implement quality standards.
Key Responsibilities:
- Lead or support moderately complex data governance, quality, and metadata initiatives.
- Conduct detailed analyses to identify data quality, integrity, and control gaps.
- Review and test data systems to ensure adherence to quality and compliance standards.
- Design and implement metrics and audits to monitor data quality across systems.
- Recommend solutions for resolving data quality issues, leveraging domain expertise.
- Collaborate across teams to develop enterprise-wide data governance strategies.
- Act as a liaison between data teams and stakeholders to assess data needs and issues.
- Guide less experienced team members and lead project coordination efforts.
- Maintain data dictionaries, metadata repositories, and business glossaries.
Required Qualifications:
-
4+ years of experience in Data Management, Business Analysis, Analytics, or Project Management, or equivalent combination of training and work experience.
Desired Qualifications:
- Strong knowledge in Master Data Management (MDM), metadata management, and data lineage.
- Proficiency in T-SQL, data warehousing, ETL concepts, and data pipelines.
- Hands-on experience with Teradata, Snowflake, Python, Alteryx, and LLMs.
- Understanding of regression and clustering techniques.
- Skilled in using BI tools like Tableau, Power BI, or MicroStrategy.
- Familiarity with cloud platforms, especially Google BigQuery and cloud data infrastructure.
- Agile development practices, tools such as JIRA, and experience with Scrum/Kanban.
Job Expectations:
- Oversee and improve ETL and data pipeline design.
- Align data definitions and structures across multiple systems.
- Perform root cause analysis on data quality issues and implement solutions.
- Partner with engineering and architecture teams to streamline data processes.
- Ensure documentation of all metadata, dictionaries, and data usage standards.