AVEVA Hiring: Sales Data Quality Analyst 2026:-
AVEVA Recruitment 2026: AVEVA is seeking detail-oriented professionals for the position of Sales Data Quality Analyst in Bangalore or Hyderabad, India.
| Company: | AVEVA |
| Job Title: | Sales Data Quality Analyst |
| Employment Type: | Full-Time, Regular |
| Experience Required: | 4+ years of relevant experience |
| Salary: | As per Company Standards |
| Application Deadline: | ASAP |
Role Overview:-
The Sales Data Quality Analyst is responsible for maintaining the accuracy, completeness, consistency, and reliability of sales deal data. The analyst reviews sales opportunities, validates important deal attributes, identifies inconsistencies, and works with Sales, Finance, IT, and other stakeholders to resolve data quality issues.
The role supports accurate business reporting, financial analysis, sales performance tracking, and decision-making by ensuring that sales records reflect the correct commercial and contractual information.
Key focus areas include:
- Sales deal data validation and cleansing
- Data profiling and anomaly detection
- Annual Contract Value (ACV) and Total Contract Value (TCV) validation
- New business and renewal deal classification
- Sales opportunity and account segment validation
- Contract dates and deal duration checks
- SPIF attribute validation
- Root cause analysis and preventive controls
- Reporting automation and process improvement
Educational Requirements:-
Candidates should have:
- A bachelor’s degree in Business Administration, Information Systems, or a related field.
- At least 4 years of relevant experience in data analysis, sales operations, or a related role.
- Strong analytical skills and attention to detail.
- Proficiency in Microsoft Excel or similar data analysis tools.
- Familiarity with Salesforce or other CRM systems, preferably.
- A basic understanding of sales processes, commercial agreements, and deal structures.
- Strong communication and cross-functional collaboration skills.
Additional qualifications in data analytics, business intelligence, or sales operations may be beneficial, although specific certifications are not mentioned in the supplied job description.
Key Skills:-
Technical Skills
- Microsoft Excel and advanced spreadsheet analysis
- Data profiling and data quality checks
- Data cleansing and standardization
- Data validation and reconciliation
- Sales data analysis
- Salesforce or other CRM platforms
- Data anomaly and outlier identification
- Data quality reporting and documentation
- SQL — desirable
- Power Query — desirable
- VBA and Excel macros — desirable
- Dashboard development and reporting automation — desirable
- AI-powered tools for process automation — desirable
- Data flow analysis across business systems — desirable
Functional Skills
- Sales operations and commercial data management
- Sales deal validation
- New business and renewal analysis
- ACV and TCV validation
- Opportunity and account segmentation
- New logo classification
- Contract date validation
- SaaS pricing model awareness
- Cross-sell, upsell, downsell, and price uplift concepts
- Sales data reconciliation
- Root cause analysis
- Data governance and quality control
- Stakeholder coordination
- Process improvement and operational efficiency
Soft Skills
- Strong attention to detail
- Analytical and critical-thinking abilities
- Problem-solving skills
- Effective written and verbal communication
- Cross-functional teamwork
- Time management and prioritization
- Accuracy and accountability
- Structured documentation
- Ability to investigate discrepancies
- Adaptability and continuous improvement mindset
- Ability to manage repetitive validation activities consistently
Roles & Responsibilities:-
1. Sales Data Profiling and Analysis
- Conduct detailed data profiling to identify anomalies, inconsistencies, missing information, and outliers in sales deal records.
- Analyze sales data to identify potential errors that could affect business decisions and financial outcomes.
- Review sales opportunity records to ensure that critical fields are populated accurately.
- Investigate unusual values, inconsistent classifications, and discrepancies across sales records.
- Identify patterns in recurring data quality issues and recommend corrective actions.
- Support the maintenance of reliable and consistent sales data across business systems.
2. Sales Deal Data Quality Validation
- Perform comprehensive data quality checks to confirm the accuracy and completeness of sales deal information.
- Validate conversion rates and other important sales deal attributes.
- Check new business and renewal splits to ensure correct categorization.
- Validate incremental Annual Contract Value (ACV) and Total Contract Value (TCV).
- Review deal spans and other deal-related attributes for consistency.
- Ensure that sales records align with established business rules and reporting requirements.
- Identify incorrect, incomplete, or inconsistent records and coordinate their correction.
3. Data Cleansing and Standardization
- Develop and implement data cleansing procedures to improve sales data accuracy.
- Standardize sales data fields and classifications across records.
- Correct inconsistencies in sales deal information according to approved business rules.
- Maintain consistency in data entry, field usage, and sales record categorization.
- Support the implementation of repeatable data validation processes.
- Recommend improvements to reduce recurring data errors and manual rework.
- Help maintain reliable data for downstream reporting and analysis.
4. Sales Opportunity and Account Classification
- Validate the classification of new logos and customer account segments.
- Ensure sales opportunities align with organizational classification criteria and commercial strategies.
- Review closed-lost renewal deals to confirm that they are categorized correctly.
- Identify discrepancies between renewal opportunities and new business opportunities.
- Verify that deal categorization accurately reflects the nature of the sales transaction.
- Coordinate with relevant stakeholders to resolve classification issues.
- Maintain consistency across sales opportunity records and account segments.
5. Contract, Opportunity Date, and SPIF Validation
- Verify opportunity close dates and start dates for accuracy and consistency.
- Validate dates against renewal timelines and applicable contractual terms.
- Identify date-related discrepancies that could affect renewal validation or reporting.
- Ensure the accuracy and completeness of Sales Performance Incentive Fund (SPIF) attributes and flags associated with sales deals.
- Investigate missing, incorrect, or inconsistent deal attributes.
- Coordinate with business stakeholders to correct issues and prevent future discrepancies.
- Maintain accurate records for sales performance tracking and related business processes.
6. Root Cause Analysis and Preventive Controls
- Conduct root cause analysis to identify the underlying causes of sales data quality issues.
- Investigate recurring discrepancies and determine whether they result from process gaps, incorrect data entry, inconsistent definitions, or system-related issues.
- Recommend preventive measures to reduce repeated errors.
- Collaborate with Sales, Finance, IT, and other teams to implement corrective actions.
- Monitor data quality improvements after corrective measures are introduced.
- Support the development and refinement of data quality rules and controls.
- Document recurring issues, findings, resolutions, and preventive actions.
7. Data Documentation and Governance
- Maintain comprehensive documentation of data quality rules, validation procedures, and standard operating processes.
- Record identified data quality issues and their resolutions.
- Document business definitions and rules related to deal classification, renewals, ACV, TCV, and other critical sales attributes.
- Maintain clear records of validation activities and exceptions.
- Support consistent application of data governance standards.
- Help stakeholders understand data requirements and validation expectations.
- Contribute to process documentation and knowledge sharing across teams.
8. Stakeholder Collaboration
- Collaborate with Sales, Finance, IT, and other cross-functional teams to understand business and data requirements.
- Communicate data quality issues clearly to the appropriate stakeholders.
- Coordinate the investigation and resolution of discrepancies in sales records.
- Ensure data quality processes remain aligned with commercial and financial reporting needs.
- Participate in discussions about business rules, sales processes, and deal structures.
- Provide updates on validation findings, recurring issues, and corrective actions.
- Support consistent interpretation of sales data definitions across departments.
9. Reporting Automation and Process Improvement
- Identify opportunities to automate repetitive data validation, reconciliation, and reporting tasks.
- Use advanced Excel formulas, macros, VBA, Power Query, SQL, or other suitable tools where applicable.
- Develop or support data validation reports and dashboards to monitor data quality.
- Use AI-powered tools where appropriate to improve operational efficiency and reduce manual effort.
- Identify opportunities to streamline data cleansing and standardization workflows.
- Support improvements in data governance, accuracy, and reporting consistency.
- Recommend practical solutions to reduce manual errors and improve turnaround time.
Good-to-Have Skills and Knowledge
SaaS Products and Pricing Models
- Understanding of Software-as-a-Service (SaaS) products.
- Familiarity with subscription-based pricing.
- Awareness of one-time cost and pay-as-you-go pricing models.
- Understanding of how pricing structures influence sales deals and contract values.
Sales Strategies
- Knowledge of cross-selling and upselling.
- Understanding of downselling and price uplift.
- Awareness of new business acquisition and renewal processes.
- Familiarity with sales opportunity classification and customer account segmentation.
Inventory and Commercial Data
- Experience or familiarity with quotes, purchase orders, invoices, deal registrations, licenses, and contracts.
- Understanding of how commercial information is captured and maintained.
- Awareness of the relationship between sales transactions, customer contracts, and billing information.
Systems, Reporting, and Automation
- Understanding how sales data moves between business systems and a centralized database or CRM.
- Advanced Excel, formulas, VBA, and macros.
- SQL and Power Query for data extraction and transformation.
- Dashboard and reporting solution development.
- Data reconciliation and validation automation.
- AI-powered tools for repetitive task automation and operational efficiency.
About the Company:-
AVEVA is a global industrial software company providing digital solutions to industries such as energy, infrastructure, chemicals, and minerals. Its software helps organizations improve operational efficiency, sustainability, productivity, and business performance.
AVEVA has more than 6,500 employees across over 40 countries and serves thousands of enterprises worldwide.
The company’s Commercial organization supports customer relationships and business growth through sales, technical expertise, customer support, solution architecture, business operations, and customer success functions.
AVEVA Hiring: Sales Data Quality Analyst 2026 Application Process:-
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
