Posted last month
ETL QA
@ Kumaran Systems
Toronto, Ontario
Overview
Kumaran Systems is hiring an ETL QA Engineer to validate large-scale data pipelines built on Databricks and PySpark, ensuring data quality and using AI-assisted tools to improve testing. The role also contributes to AI-powered knowledge bases.
Responsibilities:
- Validate large-scale data pipelines on Databricks and PySpark
- Ensure data quality through validation and reconciliation
- Use GitHub Copilot and GitHub Actions to improve testing efficiency
- Contribute to AI-powered knowledge bases and documentation
Requirements summary:
- Hands-on Databricks and expert PySpark experience
- Solid ETL/ELT and data warehousing knowledge
- Experience with GitHub CI/CD pipelines
- Prompt engineering experience with LLMs
- Python testing frameworks such as pytest or unittest
Tools & skills
Source and dates
Source type not verified · zoho
- Employer posting date
- 2026-09-22
- First seen by our source
- 2026-10-27
- Source record updated
- 2026-10-27
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Department | Quality assurance |
| Hours per week | 40 (estimated) |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Kumaran Systems
Saved company profile
Historical company information from a saved The Jobs API profile (LinkedIn/Crunchbase fields). It is not live-verified; figures may have changed. Missing values remain unknown.
Empowering organizations in re-imagining their core operations through modern solutions.
Full company description
Kumaran Systems: Engineering Future-Ready Transformations Since 1992, Kumaran Systems has been helping enterprises bridge the past and the future — reimagining legacy systems and embracing AI-led possibilities. With over 34 years of delivery excellence, we specialise in re-engineering core applications, driving cloud transformation, and building automation-first, GenAI-enabled ecosystems across the Banking and Financial Services, Insurance, Telecom, and Automotive sectors. We help organisations not just upgrade their systems but reimagine what’s possible. What Sets Kumaran Apart Client-First, Long-Term Focus: Many of our client relationships span over 20 years — built on trust, transparency, and shared outcomes. AI-Led Automation: We design intelligent, low-touch workflows that enhance productivity, accuracy, and digital self-reliance. Legacy Modernisation at Scale: Thousands of successful migrations executed with speed, precision, and minimal disruption. Cloud-Ready Transformation: Deep expertise in re-platforming and re-architecting for scalable, cloud-native performance. Enterprise QA Solutions: Our proprietary platforms — KTAC (Codeless Test Automation) and DQV (Data Quality Validation) — accelerate testing cycles and reduce production defects. Vertical Depth, Domain Insight: Solutions grounded in decades of experience tackling complex, industry-specific challenges and compliance requirements. Why Kumaran? We blend engineering discipline with AI innovation — helping clients modernise with confidence, automate with clarity, and scale with purpose. Our global delivery model ensures agility, responsiveness, and seamless collaboration — with clients always at the heart of every engagement. At Kumaran, we don’t just solve problems — we engineer future-ready transformations.
- LinkedIn followers
- 156,535
- People associated on LinkedIn
- 1,487
- Reported office locations
- SIPCOT IT Park, Plot No.28/A-41, III Main Road, 6th Cross, Chennai, Tamilnadu 603 103, IN
- 703 Evans Avenue, Suite 605, Toronto, Ontario M9C 5E9, CA
- 101 Jefferson Drive, Suite 237, Menlo Park, California 94025, US
- Plot No. 118, Road No. 3, EPIP Phase-I, Ground floor, Neil Rao Towers, Bangalore, Karnataka 560066, IN
- 8th Floor (Level 9), “ALTIUS C” Block, Olympia Technology Park, SIDCO Industrial Estate, Guindy, Chennai, Tamil Nadu 600032, IN
- Dubai Silicon Oasis, Dubai Digital Park Building A1, Dubai, Dubai, AE
- Mahaveer Gateway, Madhava Reddy Colony, Makthakousarali, Gachibowli, Hyderabad, Telangana 500032, IN
- Business categories
- Information Technology
- Computer Hardware Development
- Other recorded names
- Kumaran Systems
- Specialties
- Application Development and Maintenance
- Testing
- Banking
- Telecom and Mobility
- Insurance
- Retail
- Oracle Migrations
- Mainframe Migration
- Artificial Intelligence
- Application Migration
- Application Modernization
- Digital transformation
- Data & Analytics Services
- AI Consulting
- AI Solutions
- and Product Development
- Profile identifies a recruitment agency
- No
- Reported total investment
- 0
Currency and observation date were not supplied; this is not a current valuation or a funding round.
Kumaran Systems: Engineering Future-Ready Transformations Since 1992, Kumaran Systems has been helping enterprises bridge the past and the future — reimagining legacy systems and embracing AI-led possibilities. With over 34 years of delivery excellence, we specialise in re-engineering core applications, driving cloud transformation, and building automation-first, GenAI-enabled ecosystems across the Banking and…
| Field | Value |
|---|---|
| Industry | IT Services and IT Consulting |
| Company size | 1,001-5,000 employees |
| Headquarters | Chennai, Tamilnadu |
| Organization type | Privately Held |
| Year founded | 1992 |
| Specialties | Application Development and MaintenanceTestingBankingTelecom and MobilityInsuranceRetailOracle MigrationsMainframe MigrationArtificial IntelligenceApplication Migration |
| Open jobs on Jobsy | 18 |
| View page |
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 be responsible for validating large-scale data pipelines, ensuring data quality, and leveraging tools like GitHub Copilot, GitHub Actions/Runners, and AI agents to improve testing efficiency and coverage. You will also contribute to building and validating 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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