Job Description : 

• Develop data quality and standards: These are based on analytical research to avoid similar problems in the future. These standards will include building data validation procedures, quality checks and profiling reports. Once created, it is their duty to ensure adherence to regulatory requirements, data governance and documentation workflows.
• Work with stakeholders and Customers: This could entail informing decision-makers about the implications of issues, working with data stewards to resolve data quality problems or creating educational resources to help other organization members and clients.
• Data Cleaning and Transformation: Extract, Transform and Load data. Cleanse and transform data to improve its quality and consistency. Implement data cleansing techniques such as standardization, normalization, and deduplication. Develop and execute data quality rules and scripts to automate data cleansing processes.
• Analyses data quality issues: When problems with data integrity are discovered the Data Quality Analyst will analyze and determine the root cause. This may involve working with other SMEs of the organization such as data stewards to understand how the data is collected, processed, and stored.
• Data Validation and verification: Validate data against business rules and industry standards. Verify data accuracy through cross-referencing. Implement data validating procedures to ensure data integrity.
• Identifying data quality issues: This entails using a variety of tools and techniques such as data profiling and cleansing. It is likely that they will work with other SMEs to develop metrics that can be used to measure the accuracy, completeness, and consistency of the data.
• Monitor data quality: This amounts to assessing and reviewing the data to ensure that it meets the organization's standards. This may involve developing and running tests or using dashboards to track the health of the data.
• Collaborate with other data professionals: To ensure that data quality is integrated across the entire data lifecycle, it is necessary for them to work closely with other professionals including data analysts and engineers. This may involve collaborating on data modelling and working with data architects to ensure that the structure and quality is considered during the designing and implementation of systems.
• Create and distribute ad-hoc reports, standard reports, and dashboards based on requestor's detailed requirements.
• Supports Leadership with ad-hoc assignments.

Additional Skills & Qualifications

• Bachelor's Degree, preferably in statistics, computer science, or business administration
• 3-5 years experience of in-depth analytical and use of reporting tools with large data sets.
• 3-5 years of experience in related role.
• Proficient in MS Office with highly developed skills in Excel. Proficiency in using spreadsheets to arrange, manipulate, format, and prepare large amounts of information. Proficiency in Python, SQL and power BI.
• Ability to use and learn a variety of query and reporting tools
• Ability to respond to requests accurately and in a timely manner
• Ability to identify issues within highly detailed data
• Ability to develop a solution and/or business process
• Effective interpersonal and communication skills
• Highly effective analytical and problem-solving skills - Sees multiple relationships between different sources of data, identifying several likely causes or consequences of a situation. Thorough understanding of Big Data Analytics and processes.
• High degree of accuracy and attention to detail.
• Ability to work in group dynamics and stand out during individual projects
• Good time management skills
• Strong multi-tasking abilities