Jobs in Vaughan

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Within 25 km of Vaughan

Added 3 days ago
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Java or Golang (Go)
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Toronto, ON
Added 3 days ago
ETL Testing
Kumaran Systems
Toronto, ON
Added 3 days ago
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Kumaran Systems

ETL Testing

Toronto, ON, Canada

Full-time

Within 25 km of Vaughan · About 22.9 km from the city centre

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Job details

Job type: Full-time
Apply by: Nov 22, 2026
Posted: Oct 23, 2026

Original job description

Role Overview

We are looking for an ETL QA Engineer with strong experience in Databricks and PySpark, combined with hands-on exposure to AI-assisted development and prompt engineering. The ideal candidate will validate large-scale data pipelines, ensure data quality, and leverage tools like GitHub Copilot, GitHub Actions/Runners, and AI agents to improve testing efficiency and coverage. You will also help build and validate AI-powered knowledge bases.

Required Skills & Experience

Core Technical Skills
Strong hands-on experience in Databricks (notebooks, jobs, clusters, Delta Lake, SQL).
Expert-level PySpark skills, including transformations, actions, window functions, and optimization techniques.
Solid understanding of ETL/ELT concepts, data warehousing, and data modeling (star/snowflake schemas, dimension/fact tables).
Proven experience in data validation, data quality checks, and reconciliation techniques.
Practical experience with GitHub, GitHub Copilot, and GitHub Actions/Runners for CI/CD pipelines.
AI & Prompt Engineering
Experience crafting effective prompts for large language models (LLMs) for coding, testing, and documentation.
Exposure to agent-based architectures or tools (e.g., workflow/agent frameworks that orchestrate multi-step AI tasks).
Familiarity with AI-based tools for code analysis, test generation, or knowledge management.
Knowledge Base & Documentation
Experience building AI-driven knowledge bases or documentation systems (e.g., using vector search, embeddings, or LLM-based retrieval).
Strong documentation skills, with the ability to translate complex data/QA concepts into clear knowledge articles.
Testing & QA Practices
Strong understanding of QA methodologies: test planning, test design, defect management, and regression testing.
Experience with automated testing frameworks (Python-based testing libraries such as pytest, unittest, or similar).
Familiarity with data quality tools/practices (e.g., validation rules, thresholds, anomaly detection).

Key Attributes

Strong analytical and problem-solving skills with a detail-oriented mindset.
Passion for data quality, automation, and continuous improvement.
Ability to work in an agile, fast-paced environment and collaborate across multiple teams like Business and operations team and AD team.
Curiosity and openness to adopting new AI tools and practices for QA.

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More about this job

Responsibilities

Validate large-scale data pipelines and ensure data quality through testing, validation, and reconciliation. Use AI-assisted development tools and prompt engineering to improve testing efficiency and coverage, and help build and validate AI-powered knowledge bases.

Requirements

Requires strong hands-on Databricks and expert PySpark experience, along with knowledge of ETL/ELT, data modeling, data quality, and QA methodologies. Candidates should also have experience with GitHub-based CI/CD, Python testing frameworks, prompt engineering, AI tools or agents, and clear technical documentation.

Skills

Databricks
PySpark
ETL/ELT
Data Validation
Data Quality
Data Reconciliation
GitHub
GitHub Copilot
GitHub Actions
Prompt Engineering
Large Language Models
AI Agents
Python Testing
Pytest
Data Warehousing
Knowledge Bases

Visa sponsorship

Not detected in the job text

Keywords

ETL Testing
ETL QA
Databricks
PySpark
Delta Lake
SQL
ETL/ELT
Data Warehousing
Data Modeling
Star Schema
Snowflake Schema
Data Validation
Data Quality
Data Reconciliation
GitHub
GitHub Copilot
GitHub Actions
CI/CD
Prompt Engineering
Large Language Models
AI Agents
Vector Search
Embeddings
Automated Testing
Pytest
Unittest
Regression Testing
Defect Management
Anomaly Detection
Agile

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