AstraZeneca Hiring: Analyst – Data Quality 2026:-
AstraZeneca is offering an opportunity for the position of Analyst – Data Quality in Chennai, Tamil Nadu, India. The role focuses on data quality management, SQL-based validation, Python automation, data pipeline monitoring, and business intelligence reporting.
Complete job details are provided below.
| Company: | AstraZeneca |
| Job Role: | Analyst – Data Quality |
| Career Level: | C3 |
| Department: | Data / Analytics / Data Management |
| Job Category: | Data Quality and Analytics |
| Location: | Chennai, Tamil Nadu, India |
| Application Closing Date: | 13 October 2026 |
| Experience Required: | Experience in data quality, data analytics, data management, or related data roles |
| Work Mode: | Hybrid, with an average minimum of three days per week in the office |
About the Company:-
AstraZeneca is a global, science-led, patient-focused pharmaceutical company committed to the research, development, and commercialisation of prescription medicines.
With approximately 90,000 employees across 85 countries, AstraZeneca works to advance scientific innovation and improve outcomes for patients worldwide. The company brings together professionals from clinical development, regulatory affairs, medical affairs, finance, IT, digital, manufacturing, and supply operations.
AstraZeneca promotes collaboration, diversity, innovation, and continuous learning. Its teams use modern technologies and data-driven approaches to improve business operations and support meaningful outcomes for patients.
Role Overview:-
As an Analyst – Data Quality, you will help ensure that enterprise data products are accurate, complete, consistent, and reliable for analytics and reporting.
You will implement and monitor data quality controls across shared data platforms, develop SQL validation queries, automate checks using Python, and investigate data issues across source systems, ingestion pipelines, and transformation layers.
The role involves collaborating with data engineers, Data Product Managers, business stakeholders, market teams, and governance specialists to resolve data quality issues and improve trust in enterprise data.
You will also support Power BI dashboards, data quality scorecards, metadata management, and governance processes that contribute to reliable, AI-ready data products.
Educational Requirements:-
Candidates should have a quantitative bachelor’s degree or equivalent relevant experience in one of the following fields:
- Engineering
- Computer Science
- Data Science
- Statistics
- Applied Mathematics
- Economics
- Other related quantitative disciplines
Note: The provided job description does not specify a mandatory postgraduate degree or an exact number of years of experience.
Key Skills:-
Technical Skills
- SQL Programming
- SQL Joins and Aggregations
- SQL Query Optimisation
- Python Programming Fundamentals
- Data Validation and Data Profiling
- Data Quality Management
- Data Quality Monitoring
- ETL/ELT Pipeline Operations
- Data Warehousing
- Data Lake Technologies
- Databricks
- Data Transformation and Processing
- Data Analysis and Reporting
- Power BI Dashboard Development
- Microsoft Excel
- Data Quality Scorecards
Data Quality and Analytics Skills
- Data Completeness and Accuracy Checks
- Data Consistency and Reliability Validation
- Data Quality Rules and Controls
- Automated Data Validation
- Data Health Monitoring
- Root Cause Analysis
- Issue Triage and Defect Investigation
- SLA Monitoring and Reporting
- Data Quality Metrics
- Data Observability
- Data Governance
- Metadata Management
- Data Contracts and Metadata-Driven Validation
- Documentation of Validation Logic
Cloud and Data Platform Skills
Exposure to one or more of the following platforms is desirable:
- Microsoft Azure
- Amazon Web Services (AWS)
- Snowflake
- Amazon Redshift
- Databricks
- Data Warehouses and Data Lakes
Business Intelligence and Reporting Skills
- Power BI Reports and Dashboards
- Data Quality Monitoring Reports
- Critical Metric Scorecards
- Data Trend Analysis
- Issue Backlog Tracking
- SLA Adherence Reporting
- Data Interpretation and Presentation
- Business Performance Reporting
Soft Skills
- Strong analytical and logical reasoning
- Problem-solving and troubleshooting
- Attention to detail
- Written and verbal communication
- Documentation skills
- Stakeholder management
- Customer collaboration
- Teamwork and coordination
- Time management
- Continuous learning and improvement
- Ability to communicate technical findings in business terms
Roles and Responsibilities:-
1. Data Quality Management
- Implement data validation and quality rules using SQL to ensure data completeness, accuracy, and consistency.
- Develop reusable SQL scripts and queries to automate data quality checks.
- Perform data profiling to identify missing, duplicate, inaccurate, or inconsistent records.
- Monitor data quality across shared enterprise data platforms.
- Improve validation coverage and strengthen data quality controls.
- Help maintain reliable datasets for downstream analytics and reporting.
2. Data Quality Monitoring and Reporting
- Build and maintain Power BI dashboards, critical metric scorecards, and data quality monitoring reports.
- Track data health trends, SLA adherence, and outstanding data quality issues.
- Monitor quality metrics and identify areas requiring corrective action.
- Help customers and business users access and interpret data quality reports.
- Communicate monitoring results to relevant stakeholders.
- Support data-driven decisions by providing accurate and timely quality information.
3. Issue Triage and Root Cause Analysis
- Investigate data quality issues across source systems, ingestion pipelines, and transformation layers.
- Identify the root causes of missing, inaccurate, duplicated, or inconsistent data.
- Collaborate with data engineers and upstream teams to investigate defects.
- Validate fixes and confirm that reported issues have been resolved.
- Recommend preventive measures to reduce recurring data defects.
- Help improve the reliability of enterprise data products.
4. ETL/ELT Pipeline Management
- Support ETL/ELT pipeline operations and data quality monitoring.
- Work with data warehouses, data lakes, and Databricks environments.
- Review data processing and transformation workflows for quality issues.
- Identify opportunities to strengthen validation controls within data pipelines.
- Monitor data movement between source systems and downstream data platforms.
- Collaborate with engineering teams to improve pipeline reliability and data consistency.
5. Python Automation and Query Optimisation
- Develop Python scripts for data processing, validation, and routine automation.
- Automate repetitive data quality checks to reduce manual effort.
- Optimise SQL queries to improve performance and efficiency.
- Identify opportunities to streamline ETL/ELT quality controls.
- Improve the efficiency and coverage of data quality monitoring processes.
- Contribute to continuous improvement initiatives that reduce defects over time.
6. Documentation and Data Governance
- Maintain documentation for data quality rules, validation logic, benchmark definitions, and monitoring processes.
- Support metadata tagging and data classification activities.
- Assist with taxonomy and ontology alignment for AI-ready data products.
- Work with product managers and governance teams to maintain consistent data standards.
- Support metadata-driven validation and data governance practices where applicable.
- Help ensure that data quality processes are documented and consistently followed.
7. Stakeholder and Customer Collaboration
- Work with senior team members, Data Product Managers, market squads, and central governance teams.
- Collaborate with data engineers and business partners to establish data quality standards.
- Help prioritise data remediation activities based on business requirements.
- Communicate data issues, investigation results, and resolution updates.
- Support customers in understanding data quality metrics and monitoring reports.
- Build trust in enterprise data through consistent validation and transparent reporting.
8. Business Impact and Continuous Improvement
- Translate technical findings into business-relevant recommendations.
- Improve the reliability of dashboards, reports, and critical business metrics.
- Identify opportunities to prevent recurring data quality issues.
- Support faster and better-informed business decisions.
- Improve the accuracy and consistency of downstream analytics.
- Contribute to trusted, scalable, and AI-ready enterprise data products.
Essential Skills and Experience
Candidates should have relevant experience or capabilities in the following areas:
- Data quality, data analytics, data management, or related data roles.
- Implementing data validation checks, data profiling, and data quality monitoring.
- ETL pipeline operations and management.
- Hands-on experience with a data warehouse, data lake, and Databricks.
- Strong SQL skills, including joins, aggregations, and query optimisation.
- Basic to intermediate Python scripting for automation or data processing.
- Strong documentation and communication skills.
- Ability to collaborate with customers, technical teams, and business stakeholders.
Desirable Skills and Experience
The following skills and experience are desirable:
- Experience with data observability tools or monitoring frameworks.
- Exposure to AWS, Microsoft Azure, Snowflake, or Amazon Redshift.
- Experience with data pipelines and ETL/ELT processes.
- Familiarity with metadata-driven validation or data contracts.
- Knowledge of enterprise data governance platforms such as Collibra.
- Experience working in large global organisations or regulated industries.
- Exposure to pharmaceutical, healthcare, or commercial analytics datasets.
- Experience using Power BI, Excel, or other BI tools for data quality monitoring and reporting.
Eligibility Criteria:-
- Must have a quantitative bachelor’s degree or equivalent relevant experience.
- Must have relevant experience in data quality, analytics, data management, or a related field.
- Must understand data validation, profiling, or quality monitoring concepts.
- Must possess strong SQL skills.
- Must have basic to intermediate Python knowledge.
- Must have experience with ETL pipeline operations and management.
- Must have hands-on experience with a data warehouse, data lake, and Databricks, as specified in the job description.
- Must demonstrate good analytical, troubleshooting, and problem-solving abilities.
- Must possess strong documentation, communication, and collaboration skills.
- Experience with Power BI, cloud platforms, and data governance tools would be an advantage.
AstraZeneca Hiring: Analyst – Data Quality 2026 Application Process:-
Interested candidates can apply from the given below link.
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
