Latest Employment Opportunities at Data Radian Technologies | Data Quality Analyst | Recruitment 2025 

Job Description:

Data quality analysts assess the quality of data used by organisations to make educated decisions. They analyse complex data to increase the efficiency and quality of data collection, handle data quality issues, and work with database engineers to improve systems and database designs.

The Data Quality Analyst will work closely with the Management team to offer quality assurance supervision (data error detection and correction) on business operations involving data collection, storage, transformation, or use. Specifically, you will be able to help the team integrate data assets from several systems and sources (both external and internal) into the company’s systems, ensuring and monitoring the final quality of our internal data stores and flows. Your strong data analysis skills will ensure that the data is clean enough to be used by analytic systems and application code, allowing us to maintain our high-quality product and service standards. 

Salary	3-6 Lakhs Per Annum
Educational Requirement	ANY GRADUATION
Work Arrangement	WORK FROM HOME
Gender Preference	BOTH MALE AND FEMALE
Skills Requirement	Communication skills , logical approach, SQL, Critical Thinking, Analytical Thinking , Snowflake
Experience Requirement	0- 2 years
Location	Hyderabad/Secunderabad

What you’ll do 

As a Data Quality Analyst to help maintain excellent data quality and improve it throughout the organization. Your primary responsibility will be to ensure the accuracy, completeness, dependability, and trustworthiness of the data gathered from various systems and sources. To monitor data pipelines, identify issues, and devise effective solutions, you will collaborate closely with data engineers, analysts, business stakeholders, and IT teams. As part of your work, you will: verify and monitor data across several systems to ensure accuracy and consistency. Identify, investigate, and resolve issues with data quality, such as inconsistent, duplicate, or missing data. Create and maintain dashboards and reports on data quality to monitor trends and identify potential hazards. Collaborate with data engineers and business teams.

Responsibilities 

1. Verification of Information

• Cross-reference data entries with several internal and external sources to ensure their completeness and accuracy.
• To identify irregularities, duplicates, and discrepancies in datasets, conduct routine audits.
• To efficiently verify vast volumes of data and spot inconsistencies, use automated tools and scripts for additional analysis. 

2. Data Quality Assurance  

• Develop and implement standardised data quality assessments for all incoming and existing datasets.
• Watch for adherence to corporate rules, regulatory obligations, and standards while processing data.
• To identify and address formatting issues, missing numbers, and data mistakes, create data profiling procedures.

3. Data Integrity

• To ensure accountability and transparency, set up controls to track data changes and lineage throughout its existence.
• Maintain the dependability of data assets while preventing unauthorised access or modifications.
• To ensure data consistency schedule regular source and goal software checkups.

4. Problem Identification and Resolution 

• To spot issues with data quality, such as loss, corruption, or delays, keep a careful check on data pipelines.
• Perform in-depth root cause studies to determine the source of recurring data problems.
• Collaborate with the relevant teams to create and carry out remedial actions, then evaluate their success afterwards.

5. Collaboration

• Speak with business stakeholders to gather needs for data quality and to find out about key data aspects.
• Work together with the engineering and IT departments to develop and enhance data validation and cleansing protocols.
• Participate in cross-functional meetings to harmonise data quality standards and promote best practices across departments.

6. Documents

• Maintain detailed documentation of all data quality processes, including methodology, validation guidelines, and workflow diagrams.
• Note every issue that has been identified, as well as its root causes, fixes, and outcomes.
• Process improvements and changes should be reflected in revised training materials and data quality manuals.

7. Observation and Documentation

• Make and oversee interactive dashboards that display significant data quality trends and metrics.
• Send management regular reports that identify areas for improvement, data quality issues, and progress.
• Set up automated warnings and notifications for noteworthy irregularities or data quality violations.

8. Data Governance 

• Take part in the creation and revision of frameworks, policies, and procedures related to data governance.
• Enforce data stewardship roles and duties throughout the whole company.
• Participate in data governance committees to ensure consistency with regulatory standards and organisational objectives.

Required Qualification:

• A bachelor’s degree.
• strong precision and attention to detail when working with data.
• knowledge of data profiling and SQL for data analysis and validation.
• outstanding intellectual and problem-solving abilities.
• strong communication and teamwork skills.
• the capacity to manage deadlines and operate autonomously.
• knowledge of the fundamentals of data management, such as data integrity, governance, and quality.
• It is advantageous to have prior experience using Snowflake or data quality tools and technologies.

Key skills:

1. Communication

• Clearly explain data issues to different teams.
• Effectively cooperate with others.
• Compose clear, well-organised emails and reports.

2. Logical approach 

• Break up challenging problems into smaller, more manageable pieces.
• To spot errors or contradictions, apply systematic thinking.
• Make use of recommendations to guarantee data accuracy.

3. SQL  

• Make queries to get and check data. 
• Boost query efficiency. 
• Use functions and joins to ensure consistency.

4. Critical Thinking

• Verify and question the data before reporting.
• Analyse different methods for handling data issues.
• Predict and steer clear of impending data problems

5. Analytical Skills

• Examine the data for trends and patterns.
• Use data metrics to inform your judgements.
• Make use of tools to make your data more reliable.

6. Snowflake

• Identify how Snowflake’s data warehouse functions.
• Make an SQL query for Snowflake data checks.
• Utilise Snowflake’s capabilities to confirm the accuracy of your data.

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