QA Automation Engineer with Databricks

Vizient•Chicago, IL

About The Position

We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Requirements

Databricks jobs, Delta tables, views, data transformations, and ETL pipelines testing
Validation of metric calculations, business rules, aggregations, derived values, and source-to-target data transformations
Source-to-target reconciliation
Validation of data completeness, accuracy, consistency, timeliness, and referential integrity
Automated tests for full and incremental loads, updates, deletes, retries, exception handling, and recovery scenarios
Test data creation
SQL validation queries
Reusable utilities and reconciliation frameworks
Integration of automated data tests into CI/CD pipelines
Enhancement of test automation frameworks and processes
Functional, integration, regression, and end-to-end testing
Root cause analysis of test failures
Defect documentation

Nice To Haves

Validation of data and integrations involving Stardog, knowledge graph platforms, and other data technologies.
Testing knowledge graph structures, ontologies, relationships, mappings, SPARQL queries, data lineage, and provenance.

Responsibilities

Develop and maintain automated test suites for Databricks jobs, Delta tables, views, data transformations, and ETL pipelines.
Validate metric calculations, business rules, aggregations, derived values, and source-to-target data transformations within Databricks.
Perform source-to-target reconciliation and validate data completeness, accuracy, consistency, timeliness, and referential integrity across systems.
Develop automated tests for full and incremental loads, updates, deletes, retries, exception handling, and recovery scenarios.
Create test data, SQL validation queries, reusable utilities, and reconciliation frameworks to improve testing efficiency and coverage.
Integrate automated data tests into CI/CD pipelines and enhance test automation frameworks and processes.
Perform functional, integration, regression, and end-to-end testing across large-scale data solutions.
Analyze test failures, conduct root cause analysis, document defects, and collaborate with data engineering and platform teams to resolve issues.