QA Engineer - Onsite -Singapore Secondment

Experience Requirements
Total QA/Testing Experience: 5+ years.
Data Testing Experience: 3+ years specifically in Big Data, Hadoop, or Cloud Data Warehouse environments.
Must have knowledge or at least Databricks Experience: 1+ years of experience testing pipelines within a Databricks environment.
Automation Focus: Proven track record of moving from manual SQL checks to automated Python-based testing frameworks.
Migration automation test experience using Python

Required Certifications
Good to have: Databricks Certified Data Engineer Associate (at minimum).
Preferred: ISTQB Foundation or Advanced Level (Test Automation Engineer).

Core Technical Skills
1. Data Validation Frameworks
Great Expectations / Pandera: Proficiency in using Python-based libraries to define data "contracts" and automated validation suites.
DLT Expectations: Deep understanding of Delta Live Tables (DLT) expectations (Fail, Drop, Quarantining bad records).
Advanced SQL: Expert-level SQL for complex data reconciliation, identifying duplicates, and null-value analysis across billions of records.
2. Python for QA (PySpark)
Pytest-Spark: Experience using pytest to write unit tests for PySpark transformations and logic.
Notebook Testing: Ability to write automated test notebooks that validate Medallion Architecture transitions (Bronze to Silver, Silver to Gold).
Data Reconciliation: Building Python scripts to perform "source-to-target" counts and checksums across distributed file systems.
3. Performance Integration Testing
Scalability Testing: Ability to validate that data pipelines meet performance SLAs when data volume spikes.
End-to-End Orchestration Testing: Testing the reliability of Databricks Workflows and handling of job failures/retries.
Schema Evolution: Testing how pipelines handle upstream schema changes without breaking downstream Gold tables.
4. Goverce Security Testing
Unity Catalog Validation: Testing Row-Level Security (RLS) and Column-Level Masking to ensure unauthorized users cannot see sensitive data.
Data Lineage: Validating that data lineage in Unity Catalog correctly reflects the movement of data across the Lakehouse.

Preferred Candidate Background
"Data-First" Mindset: Understanding that testing a Lakehouse is about testing the data and its behavior, not just the "UI" or "API."
Software Engineering Foundation: Candidates who know how to use Git (Branching/Merging) to manage their test code alongside the engineering team.
Distributed Systems Knowledge: Basic understanding of Spark (shuffling, partitioning) to understand why data might be missing or duplicated in a distributed environment.

Key Responsibilities

Develop Test Strategy: Create a comprehensive test plan for the Lakehouse, focusing on Data Integrity, Accuracy, and Consistency.
Automate Validation: Replace manual "spot-checking" with automated Python test suites that run as part of the CI/CD pipeline.
Defect Analysis: Identify and document data anomalies, working closely with Data Engineers to perform root-cause analysis on Spark job failures.
Regression Testing: Ensure that new PySpark code deployments do not impact existing Gold layer business logic or dashboard reporting.