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We are seeking a Senior Data QA Analyst to support the migration of pricing and analytics data from legacy SAS processes to a Databricks and dbt platform. The role will focus on testing data sourced from a modern Insurance Platform and a legacy system, validating transformations, and ensuring downstream datasets are complete, accurate and fit for use.

You will work closely with data engineers, pricing analysts, business SMEs and delivery teams to identify defects, explain data differences and provide clear evidence for release decisions.

Apply Now > https://ingrity.com/careers/#job=data-qa-analyst-01a19f

Key responsibilities:

Develop and execute test cases for ingestion, transformation and reporting datasets across bronze, silver and gold layers.
Reconcile modern Insurance Platform and a legacy system data against source systems, transformation rules and expected business outcomes.
Validate policy, quote, risk and transaction records, including keys, dates, transaction sequences and product or brand mappings.
Test premiums, exposure, coverage, renewal and pricing attributes, including calculations and aggregation rules.
Check schema compatibility, data completeness, duplicates, null values, referential integrity, row counts and historical versus incremental loads.
Investigate discrepancies using SQL, trace records through the pipeline and document the root cause with reproducible evidence.
Support system integration testing, regression testing, UAT and production release validation.
Maintain test plans, test data, defect records and concise test completion reports.
Help develop repeatable data quality checks and automated reconciliation where practical.

Essential skills and experience:

5+ years in data testing, ETL testing or data quality assurance, including substantial hands-on testing of complex data transformations.
Strong SQL skills, including joins, window functions, aggregations and reconciliation queries.
Experience testing data pipelines and layered data platforms, preferably using Databricks, Delta Lake, Spark or a comparable environment.
Experience validating source-to-target mappings, business rules, incremental loads and downstream data products.
Ability to analyse large datasets and distinguish genuine defects from expected differences between systems.
Experience working with data engineers and business SMEs to clarify requirements and resolve defects.
Clear written communication and disciplined test evidence, defect reporting and release reporting.

Highly Desirable:

Insurance domain experience, particularly policy, quote, risk, coverage, renewal, premium or transaction data.
Experience with General Insurance data testing and/or migration from a legacy policy administration system.
Hands-on exposure to dbt, PySpark or Python for test automation and reconciliation.
Experience testing SAS-to-cloud migrations.
Familiarity with Git-based delivery, CI/CD and defect tracking tools.
Experience validating data consumed by pricing, actuarial or analytics teams.

What success looks like: You can take a business rule or source-to-target mapping, turn it into effective data tests, investigate a failed result to record level, and clearly explain whether the difference is a defect, a source-system variation or an agreed transformation. Your testing gives the team confidence to release Duck Creek and Protect data for pricing and analytics use.

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