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Data Quality Engineer

Roles & Responsibilities

  • Advanced SQL skills with ability to write and optimize queries, joins, aggregations, window functions, and reconciliation scripts
  • Hands-on ETL/ELT testing experience including source-to-target validation, transformations, CDC, and data lineage
  • Experience with PC Insurance data and systems (policies, coverages, claims, billing) and familiarity with Guidewire PolicyCenter, BillingCenter, and Claims
  • Experience with data testing frameworks and automation (e.g., Great Expectations, dbt tests, Soda Core) and scripting in Python, PySpark, or Bash

Requirements:

  • Define and maintain the overall data quality test strategy across ingestion, transformation, reconciliation, and reporting layers
  • Design and execute data quality test cases for PC insurance data (policies, coverages, deductibles, claims, billing, and transactions) and perform data reconciliation across Guidewire systems, data lakes, data warehouses, and reporting platforms
  • Leverage automation frameworks (Great Expectations, dbt tests, Soda Core) to augment manual testing, with a focus on reusability, scalability, and CI/CD integration
  • Collaborate with data engineers, analysts, and business stakeholders to capture testing requirements, validation rules, conduct data profiling and anomaly detection, and contribute to governance and regulatory compliance testing

Job description

Job Summary:

We are 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 our enterprise data platforms. This role focuses on data validation, reconciliation, and quality assurance across ingestion, transformation, and reporting layers, while also contributing to automation and continuous improvement. In addition, this role will own the data quality test strategy, ensuring standardized practices, reusable frameworks, and consistent QA coverage across all P&C data initiatives. The ideal candidate has strong SQL skills, experience with insurance data models (policies, coverages, claims, billing), and a passion for ensuring that data is both trustworthy and production ready.

Duties/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.

Required Skills/Abilities:

  • 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

Required Education and Experience:

  • 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.

Preferred Requirements:

  • 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.

Physical Requirements:

  • Prolonged periods of sitting or standing at a desk and working on a computer.

 

Salary: Starting at $100,000 annually. Candidate's skills, experience and abilities will be taken into consideration for final offer.

 

Bamboo is committed to the principles of equal employment. We are committed to complying with all federal, state, and local laws providing equal employment opportunities, and all other employment laws and regulations.

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