Location: Bengaluru
Experience: 2+ Years
Company: Wells Fargo
Employment Type: Full-Time
About this Role:
Wells Fargo is seeking a Data Management Analyst to support its enterprise data strategy. This role is ideal for professionals looking to build their career in data governance, data quality, and analytics within a globally recognized financial institution.
Key Responsibilities:
- Participate in identifying and remediating data quality and integrity issues.
- Conduct audits and benchmark data quality using defined metrics.
- Support data governance processes by documenting standards, tools, and remediation procedures.
- Assist in root cause and impact analysis through data profiling and business/data mapping.
- Collaborate with business and technical teams to maintain accurate metadata, business glossaries, and data dictionaries.
- Contribute to documentation of business requirements, design decisions, and remediation updates.
- Assist in regulatory reporting and compliance support as needed.
- Recommend and contribute to improvement plans for new data source quality assessments.
Required Qualifications:
-
2+ years of experience in Data Management, Business Analysis, Analytics, or Project Management or equivalent combination of education and work experience.
Desired Qualifications:
- Hands-on experience in enterprise data initiatives.
- Knowledge in T-SQL, data warehousing, joins, indexing, and data analysis.
- Familiarity with ETL concepts, data pipelines, and transformation logic.
- Understanding of BI concepts with experience in tools like Tableau, Power BI, or MicroStrategy.
- Knowledge of cloud platforms (BigQuery, Snowflake, etc.) and cloud-native data processing.
- Familiarity with Agile principles, Scrum/Kanban, and SDLC tools such as JIRA.
- Skills in SQL – Teradata, Snowflake, Python, Regression & Clustering, Alteryx, and LLMs.
Job Expectations:
- Assist with implementing and monitoring data management processes.
- Perform regular audits to ensure data consistency and integrity.
- Work with IT and business teams to apply data cleansing and validation logic.
- Contribute to defining metadata standards and documentation for data elements.
- Collaborate with data engineers and architects to improve data flow and optimize storage solutions.
- Investigate and resolve data quality issues through detailed analysis and reporting.