Home › Companies › Fa Etvl Saasfaprod1 Fa Ocs Oraclecloud Com CX 1 › DE&A - Core - Data Quality Management - Data Quality Management (Other)
DE&A - Core - Data Quality Management - Data Quality Management (Other)
Fa Etvl Saasfaprod1 Fa Ocs Oraclecloud Com CX 1 · PUNE CAMPUS, Pune, Maharashtra, IN · Active · $5 · Oracle Recruiting Cloud / Fusion HCM
Job facts
| Field | Value |
|---|---|
| Company | Fa Etvl Saasfaprod1 Fa Ocs Oraclecloud Com CX 1 |
| Title | DE&A - Core - Data Quality Management - Data Quality Management (Other) |
| Normalized title | - |
| Department / team | - |
| Location | Maharashtra, IN, United States |
| Work model | - |
| Employment type | - |
| Salary | $5 |
| Status | active |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
| Posted / first seen | 2026-06-18 / 2026-06-18 |
| Changed / last seen | 2026-06-22 / 2026-06-22 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Fa Etvl Saasfaprod1 Fa Ocs Oraclecloud Com CX 1. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Oracle Recruiting Cloud / Fusion HCM. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Maharashtra. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Fa Etvl Saasfaprod1 Fa Ocs Oraclecloud Com CX 1 |
| Source | e5668812-cee6-401d-baad-f458a448f385 |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
Description
Description
We are seeking a hands on QA Lead to drive quality assurance for a scalable, enterprise-wide data platform for an insurance client. The role involves validating batch and real-time data pipelines , ensuring data accuracy across Raw, Silver, and Gold layers , and supporting report rationalization and self-service analytics (Power BI) .
The QA Lead will define and implement end-to-end data testing strategies , covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring data quality, integrity, and performance optimization .
Key Responsibilities
1. QA Strategy & Leadership
Define and implement end-to-end QA strategy for the enterprise data platform Establish test frameworks, standards, and governance for data validation Lead QA planning, estimation, and execution across multiple data streams 2. Data Validation & Testing
Validate data across Raw, Silver, and Gold layers ensuring accuracy, completeness, and consistency Perform source-to-target reconciliation for batch and real-time pipelines Design and execute: Data quality checks Transformation validation (DBT models) Aggregation and KPI validation 3. Batch & Real-Time Pipeline Testing
Test batch ingestion pipelines using AWS Glue Validate real-time streaming data pipelines using Amazon Kinesis Ensure data latency, sequencing, and event consistency in streaming pipelines 4. Reporting & Rationalization QA
Validate datasets powering Power BI self-service reports Support report rationalization initiatives by ensuring consistency of KPIs and eliminating redundant data sources Perform report/data reconciliation testing across legacy vs new platform 5. Automation & Tools
Develop and implement automated data testing frameworks Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks) Enable continuous testing integration within CI/CD pipelines 6. Performance & Optimization Testing
Validate performance of: Data pipelines Queries in Snowflake Identify bottlenecks and work with engineering teams to optimize pipelines and queries Ensure scalability for large data volumes and concurrent workloads 7. Data Quality & Governance
Define and enforce data quality rules, thresholds, and monitoring Implement data anomaly detection and alerting mechanisms Ensure compliance with audit, reconciliation, and governance standards Required Skills & Experience
Core Technical Skills
Strong experience in data testing / ETL testing / data QA Hands-on expertise with: Snowflake (data validation, SQL testing) DBT (testing, model validation) AWS Glue (batch pipeline validation) Amazon Kinesis (real-time pipeline testing) Advanced proficiency in SQL for data validation and reconciliation Programming skills in Python (preferred) Testing Expertise
Experience in: Data reconciliation (source vs target) Data quality frameworks and validation techniques Automated data testing tools Understanding of medallion architecture (Raw, Silver, Gold layers) Analytics & Reporting
Experience validating Power BI reports and datasets Strong understanding of business KPIs and reporting consistency Domain Expertise (Preferred)
Experience in Insurance domain (Policy, Claims, Billing data) Familiarity with regulatory reporting, audit, and reconciliation requirements Experience
8–12 years in QA / Data Testing / ETL Testing 3+ years in QA leadership or lead role Experience working on enterprise-scale data platforms
Responsibilities
QA Lead
Qualifications
QA Lead
Company
At Zensar, we’re “experience-led everything” . We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures . Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus .
Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
Full job record
| Job ID | b513b83955c4170e207b8306dcf934b949025c92 |
| Org ID | 5a37f742-8bcb-45fc-a5cb-7cda94e88572 |
| Source ID | e5668812-cee6-401d-baad-f458a448f385 |
| Board ID | e5668812-cee6-401d-baad-f458a448f385 |
| Provider | oracle_hcm |
| Provider Job Key | 146975 |
| Title | DE&A - Core - Data Quality Management - Data Quality Management (Other) |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | PUNE CAMPUS, Pune, Maharashtra, IN |
| Department | — |
| Team | — |
| Employment Type | — |
| Workplace Type | — |
| Remote Policy | — |
| Country | United States |
| Region | IN |
| City | Maharashtra |
| Salary Raw | Description We are seeking a hands on QA Lead to drive quality assurance for a scalable, enterprise-wide data platform for an insurance client. The role involves validating batch and real-time data pipelines , ensuring data accuracy across Raw, Silver, and Gold layers , and supporting report rationalization and self-service analytics (Power BI) . The QA Lead will define and implement end-to-end data testing strategies , covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring data quality, integrity, and performance optimization . Key Responsibilities 1. QA Strategy & Leadership Define and implement end-to-end QA strategy for the enterprise data platform Establish test frameworks, standards, and governance for data validation Lead QA planning, estimation, and execution across multiple data streams 2. Data Validation & Testing Validate data across Raw, Silver, and Gold layers ensuring accuracy, completeness, and consistency Perform source-to-target reconciliation for batch and real-time pipelines Design and execute: Data quality checks Transformation validation (DBT models) Aggregation and KPI validation 3. Batch & Real-Time Pipeline Testing Test batch ingestion pipelines using AWS Glue Validate real-time streaming data pipelines using Amazon Kinesis Ensure data latency, sequencing, and event consistency in streaming pipelines 4. Reporting & Rationalization QA Validate datasets powering Power BI self-service reports Support report rationalization initiatives by ensuring consistency of KPIs and eliminating redundant data sources Perform report/data reconciliation testing across legacy vs new platform 5. Automation & Tools Develop and implement automated data testing frameworks Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks) Enable continuous testing integration within CI/CD pipelines 6. Performance & Optimization Testing Validate performance of: Data pipelines Queries in Snowflake Identify bottlenecks and work with engineering teams to optimize pipelines and queries Ensure scalability for large data volumes and concurrent workloads 7. Data Quality & Governance Define and enforce data quality rules, thresholds, and monitoring Implement data anomaly detection and alerting mechanisms Ensure compliance with audit, reconciliation, and governance standards Required Skills & Experience Core Technical Skills Strong experience in data testing / ETL testing / data QA Hands-on expertise with: Snowflake (data validation, SQL testing) DBT (testing, model validation) AWS Glue (batch pipeline validation) Amazon Kinesis (real-time pipeline testing) Advanced proficiency in SQL for data validation and reconciliation Programming skills in Python (preferred) Testing Expertise Experience in: Data reconciliation (source vs target) Data quality frameworks and validation techniques Automated data testing tools Understanding of medallion architecture (Raw, Silver, Gold layers) Analytics & Reporting Experience validating Power BI reports and datasets Strong understanding of business KPIs and reporting consistency Domain Expertise (Preferred) Experience in Insurance domain (Policy, Claims, Billing data) Familiarity with regulatory reporting, audit, and reconciliation requirements Experience 8–12 years in QA / Data Testing / ETL Testing 3+ years in QA leadership or lead role Experience working on enterprise-scale data platforms Responsibilities QA Lead Qualifications QA Lead Company At Zensar, we’re “experience-led everything” . We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures . Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus . Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status. |
| Salary Min | 4.8 |
| Salary Max | — |
| Salary Currency | USD |
| Salary Period | — |
| Source URL | https://fa-etvl-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/146975 |
| Apply URL | https://fa-etvl-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/146975 |
| First Seen At | 2026-06-18 11:46:56Z |
| Last Seen At | 2026-06-22 15:25:45Z |
| Last Checked At | 2026-06-22 15:25:45Z |
| Last Changed At | 2026-06-22 15:25:45Z |
| Inactive At | — |
| Source Posted At | 2026-06-18 06:47:07Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=oracle_hcm/board=fa-etvl-saasfaprod1.fa.ocs.oraclecloud.com|CX_1/date=2026-06-22/2026-06-22T15-24-22-600Z-2fee4eb51ab9fee905363616e840ee2d9000c768d4f4588f8b91f2893e0d6aee.json |
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"ExternalDescriptionStr": "<p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">We are seeking a <strong>hands on</strong> <strong>QA Lead</strong> to drive quality assurance for a <strong>scalable, enterprise-wide data platform</strong> for an insurance client. The role involves validating <strong>batch and real-time data pipelines</strong>, ensuring <strong>data accuracy across Raw, Silver, and Gold layers</strong>, and supporting <strong>report rationalization and self-service analytics (Power BI)</strong>.</span></span></p><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">The QA Lead will define and implement <strong>end-to-end data testing strategies</strong>, covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring <strong>data quality, integrity, and performance optimization</strong>.</span></span></p><hr><p><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 18pt;\"><strong>Key Responsibilities</strong></span></span></p><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>1. QA Strategy & Leadership</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Define and implement <strong>end-to-end QA strategy</strong> for the enterprise data platform</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Establish <strong>test frameworks, standards, and governance</strong> for data validation</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Lead QA planning, estimation, and execution across multiple data streams</span></span></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>2. Data Validation & Testing</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Validate data across <strong>Raw, Silver, and Gold layers</strong> ensuring accuracy, completeness, and consistency</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Perform <strong>source-to-target reconciliation</strong> for batch and real-time pipelines</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Design and execute: </span></span><ul style=\"list-style-type: circle;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Data quality checks</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Transformation validation (DBT models)</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Aggregation and KPI validation</span></span></li></ul></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>3. Batch & Real-Time Pipeline Testing</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Test <strong>batch ingestion pipelines using AWS Glue</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Validate <strong>real-time streaming data pipelines using Amazon Kinesis</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Ensure data latency, sequencing, and event consistency in streaming pipelines</span></span></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>4. Reporting & Rationalization QA</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Validate datasets powering <strong>Power BI self-service reports</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Support <strong>report rationalization initiatives</strong> by ensuring consistency of KPIs and eliminating redundant data sources</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Perform <strong>report/data reconciliation testing</strong> across legacy vs new platform</span></span></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>5. Automation & Tools</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Develop and implement <strong>automated data testing frameworks</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks)</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Enable <strong>continuous testing integration within CI/CD pipelines</strong></span></span></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>6. Performance & Optimization Testing</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Validate performance of: </span></span><ul style=\"list-style-type: circle;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Data pipelines</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Queries in Snowflake</span></span></li></ul></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Identify bottlenecks and work with engineering teams to <strong>optimize pipelines and queries</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Ensure scalability for <strong>large data volumes and concurrent workloads</strong></span></span></li></ul><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>7. Data Quality & Governance</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Define and enforce <strong>data quality rules, thresholds, and monitoring</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Implement <strong>data anomaly detection and alerting mechanisms</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Ensure compliance with <strong>audit, reconciliation, and governance standards</strong></span></span></li></ul><hr><p><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 18pt;\"><strong>Required Skills & Experience</strong></span></span></p><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>Core Technical Skills</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Strong experience in <strong>data testing / ETL testing / data QA</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Hands-on expertise with: </span></span><ul style=\"list-style-type: circle;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>Snowflake (data validation, SQL testing)</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>DBT (testing, model validation)</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>AWS Glue (batch pipeline validation)</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>Amazon Kinesis (real-time pipeline testing)</strong></span></span></li></ul></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Advanced proficiency in <strong>SQL</strong> for data validation and reconciliation</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Programming skills in <strong>Python (preferred)</strong></span></span></li></ul><hr><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>Testing Expertise</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Experience in: </span></span><ul style=\"list-style-type: circle;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>Data reconciliation (source vs target)</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>Data quality frameworks and validation techniques</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\"><strong>Automated data testing tools</strong></span></span></li></ul></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Understanding of <strong>medallion architecture (Raw, Silver, Gold layers)</strong></span></span></li></ul><hr><p style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 13.5pt;\"><strong>Analytics & Reporting</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Experience validating <strong>Power BI reports and datasets</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Strong understanding of <strong>business KPIs and reporting consistency</strong></span></span></li></ul><hr><p><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 18pt;\"><strong>Domain Expertise (Preferred)</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Experience in <strong>Insurance domain</strong> (Policy, Claims, Billing data)</span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Familiarity with <strong>regulatory reporting, audit, and reconciliation requirements</strong></span></span></li></ul><hr><p><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 18pt;\"><strong>Experience</strong></span></span></p><ul style=\"list-style-type: disc;\"><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">8–12 years in <strong>QA / Data Testing / ETL Testing</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">3+ years in <strong>QA leadership or lead role</strong></span></span></li><li style=\"line-height: 15pt;\"><span style=\"font-family: "Segoe UI", sans-serif;\"><span style=\"font-size: 10.5pt;\">Experience working on <strong>enterprise-scale data platforms</strong></span></span></li></ul><p> </p>",
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