Data Quality Engineer

Bamboo Insurance is seeking a technically skilled Data Quality Engineer with strong expertise in Property & Casualty (P&C) Insurance data to ensure the accuracy, reliability, and consistency of their enterprise data platforms. The role focuses on data validation, reconciliation, and quality assurance while contributing to automation and continuous improvement.

Responsibilities:

• Define and maintain the overall data quality test strategy, including coverage for ingestion, transformation, reconciliation, and reporting layers
• Design and execute data quality test cases for P&C insurance data including policies, coverages, deductibles, claims, billing, and transactions
• Perform data reconciliation across Guidewire PolicyCenter, BillingCenter, Claims systems, data lakes, data warehouses, and reporting platforms
• Conduct ETL/ELT testing including source-to-target validation, transformation checks, and schema verification
• Implement data profiling and anomaly detection to identify and resolve data issues proactively
• Collaborate with data engineers, analysts, and business stakeholders to capture testing requirements, validation rules, and align with test strategy
• Create test documentation, defect reports, regression suites, and reusable templates to standardize QA practices
• Leverage automation frameworks (e.g., Great Expectations, dbt tests, Soda Core) to augment manual testing, with a focus on reusability and scalability
• Support performance and load testing to validate pipeline efficiency
• Contribute to data governance initiatives, ensuring compliance with SOX, financial reporting, and insurance regulatory standards
• Own the regression testing strategy to ensure continuity and accuracy across releases and platform changes

Requirements:

• Strong understanding of P&C insurance data structures: policy lifecycle, coverages, claims, premiums, risk scoring
• SQL (Advanced): Strong ability to write and optimize queries, joins, aggregations, window functions, and reconciliation scripts
• ETL/ELT Testing: Hands-on experience validating mappings, transformations, CDC, and data lineage
• Test Strategy & Standards: Experience designing testing approaches, prioritizing test coverage, and defining reusable QA processes
• Automation: Familiarity with Python, PySpark, or Bash for scripting repeatable validation checks
• Data Testing Frameworks: Exposure to tools like Great Expectations, dbt tests, Deequ, Soda Core, or equivalent
• Familiarity with insurance systems (e.g., Guidewire, or legacy PAS) is a strong plus
• Cloud & Data Platforms: Experience with Snowflake, AWS (S3, Glue, Lambda, RDS, Redshift), or similar platforms (Azure Synapse, GCP BigQuery)
• Data File Formats: Understanding of JSON, Parquet, XML, and other structured/unstructured formats
• Versioning & CI/CD: Basic experience with Git and CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI)
• Monitoring & Governance: Familiarity with data pipeline monitoring (CloudWatch, Datadog), data governance tools (Collibra, Alation) and compliance testing
• Experience with BI/reporting tools like Power BI, Tableau, or Qlik
• Bachelor's degree in Computer science, Information Systems, Data Engineering, or related field or equivalent relevant experience
• 2+ years of P&C Insurance Industry experience
• 3+ years of experience in data testing, quality assurance, or data validation roles
• Master's degree in Computer science, Information Systems, Data Engineering, or related field
• 3+ years of P&C Insurance Industry experience
• 4+ years of experience in data testing, quality assurance, or data validation roles