Data Quality Analyst

Roles and Responsibilities

• Data Profiling: Profiling is the process of looking at the structure, patterns, and content of data to find problems like missing information or mistakes. It helps analysts spot hidden issues that could affect the quality of the data.
• Data Cleaning: Cleaning involves correcting or excluding inaccurate, incomplete, or irrelevant data to maintain an error-free dataset for further analysis.
• Data Validation: is carried out to ensure that the data meets predefined standards and business rules. This process confirms that data is accurate, with all required fields populated, correct data types, and values within acceptable ranges.
• Data Integration: Integration is the merging of data from different sources into a unified view. A data quality analyst makes sure that data is consistent and compatible and resolves discrepancies that arise during the integration process.
• Data Monitoring: involves continuously tracking and data to confirm its accuracy, consistency, and reliability over time. A data quality analyst keeps an eye on the data at regular intervals to ensure its accuracy and consistency so that any new problems can be rapidly identified.

Requirements

• Educational Qualifications: Engineering Degree - BE/ME/BTech/MTech/BSc/MSc. Technical certification in multiple technologies is desirable.
• Mandatory skills: Requirement gathering, Requirement writing, good communication skills, SQL (Structured Query Language), ETL (extract, transform, and load) tool knowledge for data migration. Create and update requirements artifacts such as Solution Requirements Specifications, use cases, and other artifacts as needed for incorporating in google platform. Responsible for ensuring each artifact complies with quality standards set out by the Requirements Team leadership, and for conducting and facilitating requirements review sessions to ensure understanding across business and IT groups. Recommend Data quality rules, create data profiles, perform data cleaning and validation, data integration. Responsible for maintaining those requirements through the life of the project and raising awareness with mitigating strategies when issues arise that may impact budget or timelines. Validate and get all the requirements reviewed by client teams Act as a liaison between the delivery teams that are engaged in implementing the solution and the business partners that will be using the solution, as well as interact daily with development engineers and QA analysts.

Qualifications

Desired Skills: Healthcare domain, Python, R Candidate should be fine to work in Central or Mountain times.