We are seeking a detail-oriented and data-savvy QA Engineer to join our dynamic software development team. This role has an emphasis on validating data integrity, pipeline reliability, and complex analytical insights.
Our team builds a high-throughput analytics platform that processes massive datasets to deliver dashboards and metrics from diverse data streams for the emergency call handling space. You will design, develop, and execute comprehensive test strategies that span across data ingestion, processing, storage, and front-end visualization layers.
Key Responsibilities
Data Validation & Pipeline Testing
Verify the accuracy, completeness, and consistency of data as it moves through data pipelines.
Validate data ingestion from various sources (APIs, webhooks, databases) against strict schemas.
Write SQL queries to perform data reconciliation between source systems and the data warehouse/lake.
Analytics & Report Verification
Test the business logic and mathematical correctness of aggregations, KPIs, and predictive metrics.
Ensure that user-facing charts, graphs, and dashboards accurately reflect the underlying data.
Conduct regression testing on analytics algorithms and reporting features to prevent data drift.
Test Automation & Infrastructure - Experience Advantageous
Design, build, and maintain automated testing frameworks for both APIs and UI dashboards.
Implement automated data quality checks within the CI/CD pipeline.
Mock large-scale datasets to simulate edge cases, high volume, and anomalous data.
Evaluate how the analytics platform handles heavy concurrent user loads and massive data processing spikes.
Monitor query performance and report latency bottlenecks to the engineering team.
Collaboration & Agile Processes
Partner closely with Product Owners, Data Engineers, and Report Developers to understand complex tracking requirements and data models.
Participate in sprint planning, refinement, and retrospectives to advocate for quality and testability early in the development lifecycle.
Basic Requirements
Required Technical Skills & Qualifications:
Experience: 3+ years of experience as a QA Engineer, ideally within a data-intensive, BI, or analytics product environment.
Database Proficiency: SQL skills (joins, window functions, subqueries) for deep-dive data verification.
Automation Skills: Hands-on experience with automation tools (e.g., Selenium, Cypress, Playwright) and backend/API testing tools (e.g., Postman, RestAssured).
Programming (Advantageous): Proficiency in at least one scripting language (preferably Python or JavaScript) for data manipulation and test automation.
Data Technologies: Familiarity with data warehousing concepts and tools (e.g., Synapse, Databricks, Snowflake, BigQuery, Redshift) and ETL pipelines.
Methodology: Strong understanding of Agile/Scrum development cycles and CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins).
AI: AI use is encouraged and actively used to transform our technology stack.
Soft Skills & Cultural Fit
The "Data Mindset": An obsessive attention to detailyou spot a misplaced decimal point or an omitted data row from a mile away.
Analytical Thinker: Ability to understand complex business logic and translate it into edge-case test scenarios.
Strong Communicator: Ability to clearly explain technical data discrepancies to both developers and non-technical stakeholders.