Note: The job is a remote job and is open to candidates in USA. GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to leading companies. They are seeking a highly analytical and detail-oriented Data Validation & QA Engineer to support enterprise data and analytics initiatives, focusing on validating and reconciling data across reporting ecosystems and collaborating with various teams to ensure data accuracy and completeness.
Responsibilities
- Perform end-to-end data flow testing across the complete architecture: {Source System} -->{Databricks Bronze} -->{Databricks Silver}-->{Databricks Gold} -->{Power BI Semantic Model} -->{Power BI Reports}
- Execute source-to-Databricks reconciliation, validating source data against Bronze, Silver, and Gold layers via record counts, aggregates, and metric-level checks.Validate business logic, transformation rules, field mappings, and complex calculations across all layers to ensure seamless metric alignment
- Maintain an inventory of available datasets across brands and source systems; assess historical coverage, refresh frequency, and completeness.Identify data gaps, missing datasets, and source readiness for project onboarding.Validate ingestion pipelines, transformation logic, and key data quality dimensions: Completeness, Accuracy, Uniqueness, Consistency, and Timeliness.Investigate, document root causes, and resolve data loss, duplicates, and transformation defects
- Validate Power BI semantic models against Databricks Gold datasets, verifying DAX calculations, measures, dimensions, relationships, and hierarchies against approved business definitions.Validate report visuals, filters, slicers, drill-throughs, and aggregations against business benchmarks.Ensure report functionality and row/column-level security behave properly across diverse user roles and scenario profiles.Conduct thorough pre-demo and pre-release validation testing
- Log, prioritize, track, and manage reconciliation issues and defects in Jira.Partner with engineering and reporting teams to coordinate rapid defect resolution.Validate technical fixes and execute regression testing to ensure solution integrity
- Support business users through User Acceptance Testing (UAT) and document evidence of validation results.Provide data readiness assessments for executive reviews, customer demos, and production deployments.Maintain comprehensive reconciliation artifacts, validation logs, and signoff documentation
Skills
- Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field
- 4+ years of relevant experience in Data QA, Data Validation Engineering, or BI/Analytics Quality Assurance within an enterprise environment
- Advanced SQL & PySpark: Proficiency in writing complex SQL queries and PySpark scripts to perform cross-environment data validation, aggregate checks, and source-to-target reconciliation
- Databricks Ecosystem: Hands-on experience working with Databricks Lakehouse architecture, including Medallion layers (Bronze, Silver, Gold), Delta Lake tables, and data ingestion pipelines
- Power BI Expertise: Deep understanding of Power BI semantic models, DAX measures, dimensional modeling, row-level security (RLS), and report/dashboard validation
- Data Quality Frameworks: Proven experience validating key data quality dimensions-Completeness, Accuracy, Uniqueness, Consistency, and Timeliness
- Defect Management Tools: Proficiency with Jira or similar Agile project management tools for tracking issues, logging bugs, and coordinating with engineering teams
Benefits
- Opportunities to work from home
- Flexible work schedules
- Continuing education classes
- Professional certification and training (technical, soft skills, language, and communication skills)
- Additional learning opportunities through international knowledge exchanges
- Health and life insurance
- Short-term and long-term disability insurance
- A matched contribution 401K plan
- Flexible spending accounts
- PTO and holidays
Company Overview