QA Analyst - UI & Data Validation

📍 Job Overview

Job Title: QA Analyst - UI & Data Validation

Company: Jobgether (on behalf of a partner company)

Location: India

Job Type: Full-time Contract

Category: Quality Assurance / Data Operations

Date Posted: 2026-09-03

Experience Level: 2-5 Years

Remote Status: 100% Remote

🚀 Role Summary

• This role is crucial for ensuring the quality and reliability of reporting and analytics products, directly impacting business operations and data-driven decision-making.

• Responsibilities include comprehensive UI testing of dashboards and reports, alongside rigorous backend data validation and reconciliation across various data platforms.

• You will act as a key QA resource, investigating discrepancies, identifying root causes, and collaborating with Data Engineers and Developers to ensure timely resolution of issues.

• The position requires strong SQL proficiency for data analysis and validation, coupled with experience in Agile/Scrum methodologies and defect tracking tools like Jira.

📝 Enhancement Note: While the job title is "QA Analyst - UI & Data Validation," the description heavily emphasizes data accuracy, reporting logic, and data warehousing concepts, placing this role firmly within the data operations and analytics quality assurance domain. The focus on validating data before it reaches business users and the need to reconcile data across source systems, data warehouses, and reporting platforms highlight a critical GTM and Revenue Operations enablement function.

📈 Primary Responsibilities

• Conduct end-to-end testing of reporting and analytics solutions, dashboards, and data-driven applications, ensuring full functionality, reliability, and optimal usability.

• Perform detailed data accuracy validation and reconciliation between source systems, data warehouses, and reporting platforms, identifying and flagging inconsistencies that could impact business users.

• Execute comprehensive UI testing on dashboards and reports, verifying the accuracy and functionality of filters, calculations, visualizations, navigation elements, and other user-facing components.

• Investigate data discrepancies and reporting issues thoroughly, determining root causes and collaborating effectively with Data Engineers and Report Developers to drive prompt and accurate issue resolution.

• Develop, document, and maintain detailed test plans, test cases, test scenarios, and test scripts to ensure robust and comprehensive test coverage for all assigned reporting products.

• Execute various testing types, including regression, functional, integration, and user acceptance testing (UAT), as required throughout the software development lifecycle (SDLC).

• Actively participate in Agile/Scrum ceremonies, including sprint planning, daily stand-ups, backlog refinement, and retrospectives, fostering strong collaboration with Report Developers and Product Teams.

• Utilize Jira or similar work management and defect-tracking platforms to meticulously track defects, enhancements, testing activities, and their resolutions.

• Provide primary QA support for an assigned reporting product while also offering quality assurance assistance across additional analytics products, ensuring consistent quality standards and adherence to release timelines.

• Contribute to the overall quality assurance strategy for reporting and analytics products, advocating for best practices in data integrity and user experience.

📝 Enhancement Note: The responsibilities highlight a deep dive into data integrity and reporting logic, which are fundamental to Sales and Revenue Operations. Ensuring accurate reporting is critical for forecasting, performance analysis, and strategic decision-making, making this role indirectly vital for GTM success.

🎓 Skills & Qualifications

Education: [Specific educational requirements are not detailed, but a background in Computer Science, Information Technology, Data Science, or a related field is typically beneficial for QA roles involving data and analytics.]

Experience:

• A minimum of 2–5 years of dedicated Quality Assurance experience, with a strong focus on supporting reporting, analytics, business intelligence (BI), or data-focused applications.

• Proven hands-on experience in both the User Interface (UI) testing of reports and dashboards, as well as backend data validation and reconciliation.

• Demonstrated proficiency in writing and executing complex SQL queries for data querying, validation, reconciliation, and in-depth investigation across various data systems.

• Experience in creating and maintaining comprehensive testing documentation, including detailed test plans, test cases, test scenarios, and test scripts.

• Solid understanding of data warehousing concepts, data flow validation, and the ability to effectively identify data integrity and quality issues within complex data structures.

• Practical experience working within Agile/Scrum development environments and actively participating in standard Agile ceremonies.

• Comfort and proficiency in using Jira or comparable work management and defect-tracking platforms for issue management and progress reporting.

• Exceptional analytical, troubleshooting, and root-cause analysis skills, particularly when dealing with intricate reporting logic and data discrepancies.

• Excellent written and verbal communication skills, with a proven ability to collaborate effectively with both technical (Developers, Data Engineers) and non-technical (Product Managers, Business Users) stakeholders. Required Skills:

• SQL (Advanced proficiency for data querying and validation)

• UI Testing (Dashboards, Reports, Web Applications)

• Data Validation & Reconciliation

• Test Planning & Test Case Development

• Agile/Scrum Methodologies

• Jira or similar defect tracking tools

• Analytical & Troubleshooting Skills

• Root-Cause Analysis

• Data Warehousing Concepts

• Written & Verbal Communication Preferred Skills:

• Tableau Dashboard Testing

• Power BI Reporting Environment Testing

• Experience with ETL processes and data pipelines

• Familiarity with reporting architectures

• Exposure to healthcare data, payer systems (e.g., EPIC, EZ-CAP), or related healthcare platforms

• Experience in banking, financial services, or other highly regulated environments

📝 Enhancement Note: The emphasis on SQL, data warehousing, and data validation, combined with the mention of healthcare and financial services, suggests a role that supports critical operational reporting where data accuracy is paramount. This aligns with the needs of GTM and Revenue Operations for reliable data for CRM, forecasting, and performance metrics. The preference for Tableau and Power BI directly links to common BI tools used in these functions.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies in Data Validation: Showcase instances where you identified significant data discrepancies, explained your methodology for root-cause analysis, and demonstrated how your findings led to improved data accuracy or process fixes.

• Test Plan & Test Case Examples: Provide examples of detailed test plans and test cases you've developed for reporting or data-intensive applications, highlighting your approach to comprehensive test coverage and scenario design.

• SQL Query Samples: Include anonymized examples of complex SQL queries used for data validation, reconciliation, or performance analysis, demonstrating your proficiency in data manipulation and extraction.

• Defect Tracking & Resolution Examples: Illustrate how you've documented and managed defects in Jira or similar tools, focusing on the clarity of your bug reports and the effectiveness of your collaboration in driving resolutions.

Process Documentation:

• Workflow Design & Optimization: Examples of how you've contributed to optimizing testing workflows or introduced new testing processes to improve efficiency and test coverage.

• Implementation & Automation: Documentation or discussion around how testing processes were implemented, or any efforts towards test automation (even if manual scripting examples are provided).

• Measurement & Performance Analysis: Evidence of how you've measured the effectiveness of your testing efforts, such as defect escape rates, test coverage metrics, or the impact of your QA work on release quality.

📝 Enhancement Note: For a role focused on data validation and reporting QA, a portfolio demonstrating practical application of SQL, structured test case design, and effective defect management is crucial. Candidates should be prepared to discuss their systematic approach to ensuring data integrity, which is foundational for reliable GTM and revenue operations.

💵 Compensation & Benefits

Salary Range: Based on the experience level (2-5 years), location (India), and the contract nature of the role, a competitive hourly rate or monthly salary is expected. For a 2-5 year experienced QA Analyst in India, typical ranges can be between ₹400,000 to ₹900,000 INR per annum, or approximately ₹25,000 to ₹70,000 INR per month for a full-time contract. This can vary significantly based on specific skills, the partner company's budget, and the exact duration of the contract.

Benefits:

• Full-time Contract Position: Offering a clear work structure with 40 hours per week.

• 100% Remote Work: Flexibility to work from anywhere within India, eliminating commute time and costs.

• Standard Work Schedule: Monday to Friday, 8 hours per day, with alignment to PST working hours, allowing for collaboration with teams in different time zones.

• Contract Duration: Initial contract of 6–12 months, with potential for extension, providing a stable engagement.

• Professional Development: Opportunity to gain significant experience in a growing Reporting & Analytics environment, working with enterprise reporting platforms and data-driven applications.

• Technology Exposure: Hands-on experience with in-demand tools and technologies including SQL, Tableau, Power BI, Jira, data warehousing, and modern reporting platforms.

• Cross-functional Collaboration: Opportunity to work closely with Report Developers, Data Engineers, and Product Teams throughout the SDLC.

• Ownership & Impact: Meaningful ownership of QA activities for reporting products, with potential to become a primary quality resource for specific products.

Working Hours: 40 hours per week, typically 8 hours per day, Monday through Friday. This role requires alignment with PST working hours, indicating a need for availability during specific overlap periods for team collaboration and communication.

📝 Enhancement Note: The salary estimate is based on general market research for QA Analysts with 2-5 years of experience in India, considering the remote and contract nature of the role. The PST working hours alignment is a key factor that might influence the actual compensation, as it requires flexibility for candidates in India.

🎯 Team & Company Context

🏢 Company Culture

Industry: The partner company operates within the Data & Analytics, Technology, Software, and Engineering sectors. This implies a focus on leveraging data to drive business insights and technological solutions. The role specifically supports reporting and analytics products, suggesting a data-centric culture.

Company Size: While the exact size of the partner company is not specified, Jobgether's platform often partners with a range of companies, from startups to established enterprises. The description suggests a structured team environment with dedicated Report Developers, Data Engineers, and Product Teams, indicating a mid-sized to larger organization or a well-defined department within a larger entity.

Founded: Founding date of the partner company is not provided. However, the focus on modern reporting and analytics tools suggests a company that is either established in these areas or actively evolving to meet current market demands.

Team Structure:

• Cross-functional Teams: The role involves close collaboration with Report Developers, Data Engineers, and Product Teams, indicating a matrixed or agile team structure.

• Specialization: QA Analysts are expected to become primary resources for specific reporting products, suggesting a degree of specialization within the QA function.

• Reporting Hierarchy: While not explicitly stated, QA Analysts typically report to a QA Lead or Manager, who in turn may report to a Director of Engineering or Head of Product/Operations.

Methodology:

• Agile/Scrum: The team operates within an Agile/Scrum framework, participating in standard ceremonies like sprint planning, stand-ups, backlog refinement, and retrospectives.

• Data-Driven Quality Assurance: The core of the role is ensuring data accuracy and reporting logic, emphasizing a data-driven approach to quality assurance.

• Continuous Improvement: The emphasis on root-cause analysis and collaboration suggests a culture that values continuous improvement in processes and product quality.

Company Website: [The partner company's website is not directly provided, but applications are managed through Jobgether. The overarching platform is jobgether.com.]

📝 Enhancement Note: The description points to a dynamic, data-focused environment where collaboration and technical expertise in data validation and reporting are highly valued. For candidates, this suggests an opportunity to work with modern data stacks and contribute to critical business intelligence functions.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Junior to Mid-Level QA Analyst, suitable for professionals with 2-5 years of experience. It offers a specialization in the crucial area of data and reporting quality, which is a vital component of any data-driven GTM or Revenue Operations function.

Reporting Structure: You will report to a QA Lead or Manager, working closely with Data Engineers, Report Developers, and Product Managers within an Agile team structure. This provides exposure to various functional areas and project management methodologies.

Operations Impact: Your work directly impacts the reliability and accuracy of business-critical reporting and analytics. This ensures that stakeholders across sales, marketing, and operations have trustworthy data for decision-making, forecasting, and performance analysis, thereby directly influencing revenue generation and operational efficiency.

Growth Opportunities:

• Specialization in Data QA: Deepen expertise in data validation, reporting logic, and BI tools (Tableau, Power BI), becoming a subject matter expert in data quality for analytics.

• Agile & SDLC Proficiency: Enhance skills in Agile methodologies, sprint participation, and full software development lifecycle (SDLC) processes.

• Technical Skill Expansion: Gain hands-on experience with data warehousing concepts, ETL processes, and potentially advanced SQL techniques.

• Cross-functional Exposure: Develop strong collaborative relationships and communication skills by working with engineering, product, and business teams.

• Potential for Leadership: With proven performance, there's a possibility to grow into a Senior QA Analyst role, lead QA initiatives for specific products, or transition into other data-focused roles within the operations or analytics domain.

📝 Enhancement Note: This role is a strong stepping stone for individuals looking to specialize in data quality assurance, a niche that is increasingly important for GTM and Revenue Operations. The emphasis on SQL and BI tools, coupled with Agile practices, provides a solid foundation for career advancement in data-driven roles.

🌐 Work Environment

Office Type: This is a 100% remote position, meaning there is no physical office requirement. The work environment is entirely virtual.

Office Location(s): Candidates can work from anywhere within India.

Workspace Context:

• Remote Collaboration: The primary workspace is your home office, requiring self-discipline and effective virtual communication tools.

• Technology & Tools: Access to standard office software, robust internet connectivity, and the necessary QA tools (Jira, SQL clients, BI tools) will be essential.

• Team Interaction: Collaboration occurs through virtual meetings, instant messaging platforms, and project management tools, fostering a connected, albeit distributed, team environment.

Work Schedule: A standard full-time schedule of 40 hours per week, typically 8 hours per day, Monday through Friday. A key aspect is the alignment with PST working hours, which means candidates will need to be available during a significant portion of the Pacific Standard Time business day for synchronous communication and collaboration with teams in that timezone.

📝 Enhancement Note: The requirement to align with PST working hours is a critical consideration for candidates in India. It implies a need for flexibility in daily work patterns to ensure effective overlap for meetings and real-time problem-solving with the US-based teams.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening (Jobgether AI): Your application will be processed by Jobgether's AI to quickly assess your fit against core requirements.

• Shortlisting: Top-fitting candidates are identified and the shortlist is shared directly with the hiring partner company.

• Hiring Partner Interviews: The partner company's internal team will manage subsequent interview stages. This typically includes:

  • Technical Interview: Focused on SQL proficiency, data validation techniques, and understanding of reporting logic. Be prepared to demonstrate your SQL querying skills and discuss how you approach data reconciliation.
  • Scenario-Based Interview: Discussing how you would approach specific QA challenges related to UI inconsistencies or data discrepancies in reporting.
  • Agile/Team Fit Interview: Assessing your experience with Agile methodologies and your ability to collaborate effectively with cross-functional teams (Developers, Data Engineers, Product Managers).
  • Portfolio Review (if applicable): You may be asked to walk through specific examples from your portfolio, demonstrating your process, problem-solving skills, and impact.

Portfolio Review Tips:

• Highlight Data Integrity Focus: Emphasize projects where your QA work directly ensured data accuracy, integrity, and reliable reporting.

• Showcase SQL Prowess: Include anonymized examples of complex SQL queries used for validation and reconciliation. Clearly explain the business problem and how your query solved it.

• Detail Test Case Design: Present well-structured test cases and test plans that demonstrate thoroughness and coverage for reporting and data validation scenarios.

• Demonstrate Root-Cause Analysis: For any defect you present, explain your systematic approach to identifying the root cause, not just the symptom.

• Quantify Impact: Where possible, quantify the impact of your QA efforts (e.g., "reduced data discrepancies by X%", "improved reporting accuracy leading to Y decision").

Challenge Preparation:

• SQL Challenge: Expect practical SQL exercises to test your ability to query, join, and analyze data relevant to reporting scenarios.

• Test Case Design Exercise: You might be asked to design test cases for a given reporting feature or dashboard component.

• Problem-Solving Scenarios: Prepare to discuss how you would troubleshoot a complex data discrepancy or a UI issue in a report. Focus on your methodical approach.

📝 Enhancement Note: The application process involves an AI screening by Jobgether, followed by the partner company's internal process. Candidates should tailor their applications and prepare for technical interviews focusing on SQL and data validation, along with demonstrating their collaborative and problem-solving skills in an Agile context.

🛠 Tools & Technology Stack

Primary Tools:

• Jira: Essential for defect tracking, task management, and sprint backlog management. Proficiency in creating detailed bug reports and tracking progress is key.

• SQL Clients: Various SQL clients (e.g., DBeaver, SQL Developer, pgAdmin, MySQL Workbench) will be used for querying and validating data in databases.

• BI/Reporting Tools (for testing):

  • Tableau: Experience testing Tableau dashboards is preferred, involving verification of visualizations, filters, calculations, and data connections.
  • Power BI: Experience testing Power BI reporting environments is a plus.

Analytics & Reporting:

• Data Warehousing Concepts: Understanding of data warehouse architectures, schemas, and data flow is crucial for effective validation.

• Database Querying: Proficiency in extracting and analyzing data from various database systems.

CRM & Automation:

• ETL Processes: Familiarity with ETL (Extract, Transform, Load) processes is beneficial for understanding data pipelines and potential points of failure.

• Data Pipelines: Understanding how data flows from source systems through transformations to reporting layers.

📝 Enhancement Note: The technology stack heavily emphasizes SQL and BI tools, which are central to data operations and GTM analytics. Proficiency in Jira is also a standard requirement for Agile teams. Candidates should be ready to demonstrate practical experience with these tools, particularly in the context of validating data and reporting outputs.

👥 Team Culture & Values

Operations Values:

• Data Integrity & Accuracy: A paramount value, ensuring that all validated data and reports are reliable and trustworthy, forming the bedrock of business decisions.

• Collaboration & Communication: Strong emphasis on working effectively with cross-functional teams (Developers, Data Engineers, Product Managers) to achieve shared quality goals.

• Problem-Solving & Root-Cause Analysis: A culture that encourages deep dives into issues to identify underlying causes, rather than just addressing surface-level symptoms.

• Efficiency & Timeliness: Balancing thoroughness with the need to meet release timelines and deliver quality assurance efficiently.

• Continuous Learning: Encouraging team members to stay updated with new tools, techniques, and best practices in QA and data analytics.

Collaboration Style:

• Agile & Iterative: Working in sprints, with frequent feedback loops and collaborative problem-solving sessions.

• Cross-functional Partnership: QA is integrated early and often with development and product teams, fostering a shared responsibility for product quality.

• Feedback-Oriented: Openness to giving and receiving constructive feedback on processes, code, and data quality to drive continuous improvement.

• Data-Centric Discussions: Conversations and decision-making are heavily influenced by data and factual analysis, supported by robust QA validation.

📝 Enhancement Note: The values and collaboration style emphasize a proactive, data-driven, and team-oriented approach. For a QA Analyst, this means being an active participant in the development process, not just a gatekeeper, and contributing to a culture of quality and continuous improvement.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Complexity: Navigating and validating data across potentially disparate source systems and complex data warehouses can be challenging.

• PST Time Zone Alignment: Working with teams in PST requires discipline and flexibility in scheduling to ensure effective collaboration during overlapping hours.

• Balancing Thoroughness with Speed: Ensuring comprehensive test coverage while meeting tight release deadlines requires efficient prioritization and effective test strategies.

• Evolving Reporting Needs: Keeping pace with changing business requirements and evolving reporting dashboards necessitates adaptability and continuous learning.

Learning & Development Opportunities:

• Deepen SQL Expertise: Enhance advanced SQL skills for complex data manipulation, performance tuning, and data analysis.

• Master BI Tool Testing: Become an expert in testing specific BI platforms like Tableau and Power BI, understanding their intricacies and common pitfalls.

• Gain Data Warehousing Knowledge: Expand understanding of data warehousing principles, ETL processes, and data modeling.

• Contribute to Test Automation: Explore opportunities to contribute to or learn about test automation frameworks relevant to data validation or UI testing.

• Industry Exposure: Gain insights into specific industries like healthcare or finance, understanding their unique data challenges and regulatory requirements.

📝 Enhancement Note: The challenges are typical for data-focused QA roles, particularly those involving cross-timezone collaboration. The growth opportunities are substantial, offering a clear path to specialization and advancement within the data quality and analytics domain.

💡 Interview Preparation

Strategy Questions:

• Data Discrepancy Resolution: "Describe a complex data discrepancy you encountered. What steps did you take to investigate and resolve it? What was the outcome?" (Focus on your structured approach, SQL usage, and collaboration.)

• UI Testing Approach: "How would you approach testing a new dashboard with multiple filters and visualizations? What are the key areas you would focus on?" (Highlight your understanding of user experience and data representation.)

• Agile Participation: "How have you contributed to Agile ceremonies in your previous roles? How do you ensure QA is integrated effectively throughout the sprint?" (Emphasize collaboration and proactive involvement.)

Company & Culture Questions:

• Remote Work Effectiveness: "How do you maintain productivity and collaboration in a 100% remote work environment, especially when aligning with a different time zone?" (Showcase your self-management and communication strategies.)

• Data Quality Importance: "Why is data validation and reporting accuracy critical for business operations?" (Connect your role to business impact, GTM efficiency, and revenue enablement.)

• Tool Proficiency: "How proficient are you with SQL for data analysis and validation? Can you give an example of a complex query you've used?" (Be ready to demonstrate or discuss your SQL skills.)

Portfolio Presentation Strategy:

• Structure Your Case Studies: For each portfolio item, clearly outline the problem, your approach (methodology, tools used), your solution/actions, and the quantifiable results or impact.

• Focus on Data Validation & SQL: Prioritize examples that showcase your SQL skills and your systematic approach to ensuring data integrity in reporting.

• Demonstrate Collaboration: When discussing projects, mention how you collaborated with developers, engineers, or product managers to resolve issues.

• Explain Your Process: Be ready to articulate your thought process for designing test cases, executing tests, and reporting defects.

📝 Enhancement Note: Interview preparation should focus on demonstrating strong analytical skills, deep SQL proficiency, a systematic approach to data validation, and effective collaboration within an Agile framework. Highlighting your understanding of how data accuracy impacts business decisions will be key.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Jobgether platform.

• Tailor Your Resume: Ensure your resume highlights your 2-5 years of QA experience, specifically mentioning UI testing, data validation, SQL proficiency, and experience with reporting/analytics tools. Use keywords from the job description (e.g., "data reconciliation," "SQL query," "Tableau testing," "Agile ceremonies").

• Prepare Your Portfolio: Curate 2-3 key examples that best demonstrate your skills in data validation, test case design, SQL usage, and defect management. Be ready to discuss these in detail.

• Research Jobgether & Partner Company: Understand Jobgether's AI matching process and try to infer the industry and focus of the partner company based on the job description's context (data, analytics, reporting).

• Practice Interview Questions: Rehearse answers to common QA, technical (SQL), and behavioral questions, focusing on your experience with data integrity and reporting quality. Be prepared to discuss your remote work strategies and PST timezone alignment.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.