Data QA Lead

Role & responsibilities

About the Role

Indexnine Technologies is looking for a senior Data QA Lead with deep domain knowledge in Transportation & Logistics to own end-to-end quality assurance across our data pipelines and web applications. In this role, you won't just test UI elements or write basic SQL queriesyou will be responsible for validating full-stack data lineage. You will trace data transformations directly from front-end user actions through OLTP systems, ETL pipelines, and Snowflake (OLAP) storage, down to final analytics and BI reporting dashboards.

Key Responsibilities

• End-to-End Data Lineage Testing: Validate complex, end-to-end data workflows. Ensure that data updates made on the UI correctly propagate through OLTP databases, ETL pipelines, Snowflake, and reflect accurately in BI dashboards.

• Domain Alignment: Leverage Transportation & Logistics business domain knowledge (e.g., dispatch, driver logs, route optimization, fleet telemetry, freight billing) to design realistic, high-coverage end-to-end test scenarios.

• Full-Stack Test Execution:

• UI / Functional Testing: Test front-end applications for functional accuracy and correct input captures.

• Transactional DB : Perform relational database testing (PostgreSQL/MySQL/SQL Server/MongoDB) to verify schema integrity, triggers, and transactional correctness.

• Analytical DB (Snowflake): Write complex SQL scripts to validate data warehouse models, star/snowflake schemas, staging vs. target tables, and historical tracking (SCD Type I/II).

• ETL & Data Pipeline Testing: Test batch and real-time ETL/ELT pipelines, data transformation logic, data completeness, freshness, and error-handling routines.

• BI & Reporting Validation: Validate Power BI, Tableau, or Looker dashboards for metrics calculation, slice-and-dice behavior, filter performance, and overall visualization accuracy.

• Root Cause Analysis & Debugging: Independently debug data discrepancies across the entire architecture stack. Trace data anomalies at the reporting level back to source UI inputs or pipeline transformations.

• Test Strategy & Leadership: Define testing strategies, data quality frameworks, test automation approaches (for both UI and Data validation), and mentor junior QA engineers.

Required Skills & Qualifications

• Transportation Domain Expertise: Strong understanding of transportation workflows (e.g., TMS, Freight Management, Fleet Operations, Supply Chain Analytics).
• Data Warehousing & Snowflake: Hands-on experience testing and querying Snowflake (OLAP) data warehouses. Understanding of staging layers, dimensional modeling, and performance optimization.
• Advanced SQL & ETL: Proficiency in writing complex SQL queries (joins, window functions, CTEs, aggregation) to compare source-to-target datasets and validate ETL pipeline logic.
• Database Knowledge: Experience with both OLTP databases (PostgreSQL, MySQL, MS SQL, MongoDB) and OLAP data warehouses.
• BI / Visualization Tools: Hands-on experience testing BI tools (e.g., Power BI, Tableau, Looker) for data accuracy, measures, DAX/LOD calculations, and dashboard UI responsiveness.
• Automation & Tools: Familiarity with test automation frameworks (Cypress, or Playwright for UI; dbt tests, or custom Python SQL scripts for Data QA).

Preferred Qualifications

• Experience with cloud platforms (AWS, Azure, or GCP).
• Familiarity with streaming/real-time data pipelines (e.g., Apache Kafka, AWS Kinesis).
• Knowledge of CI/CD integration for automated data quality checks.