Job description

Job Summary

Role Overview

The role focuses on validating end‑to‑end ETL pipelines built on Teradata and Informatica, ensuring data accuracy, performance, and reliability across large‑scale data‑warehouse environments. The engineer will design test strategies, build test cases, automate regression suites, and work closely with development, data engineering, and operations teams. Experience with Control‑M for job scheduling and workflow monitoring is essential.

Key Responsibilities

Key Responsibilities

Analyse business requirements, data mappings, and ETL design documents to define comprehensive test strategies for data‑warehouse solutions.
Develop and execute functional, integration, system, and regression test cases for ETL workflows.
Validate data transformations, aggregations, and load processes using advanced SQL queries.
Perform source‑to‑target data validation, data profiling, and reconciliation across large datasets.
Perform data load using Informatica PowerCenter & BMC Control-M.
Monitor, validate and troubleshoot Control-M scheduled jobs, dependencies, and batch execution flows.
Log, track, and retest defects using tools like Zephyr & JIRA.
Closely collaborate with Developers, Business analysts, Data Analysts, and Business stakeholders to clarify requirements, resolve data issues, and ensure alignment with business expectations.
Build and maintain test automation frameworks for repetitive ETL validation tasks (Python, shell scripting, or SQL‑based automation).
Work with Git Version control, performing activities such as branch creation, pulling, pushing , merging code and conflict resolution.
Document test results, create defect reports, and maintain traceability across requirements, test cases, and execution cycles.
Support UAT cycles, production validation, and post‑deployment checks.

Skill Requirements

Required Skills and Experience

Strong hands-on experience in ETL/Data Warehouse Testing.
Expertise in source‑to‑target mapping (STTM) validation, data reconciliation, and data profiling.
Advanced SQL skills (Joins, subqueries, window functions, aggregations).
Solid experience with ETL workflows/pipelines, mappings, and transformations.
Working knowledge of job schedulers like Control-M, IBM Tivoli, etc., for job monitoring, and troubleshooting.
Familiarity with data modelling concepts (star schema, snowflake, SCDs).
Experience with defect management tools such as JIRA, Azure DevOps, or HP ALM.
Ability to analyse complex data flows and identify gaps or inconsistencies.
Strong communication skills and ability to work with cross‑functional teams.
The engineer is expected to operate effectively within Agile/Scrum teams, contributing to iterative delivery of data‑warehouse and ETL capabilities.

Preferred / Nice‑to‑Have

Experience with automation frameworks for ETL testing (Python, SQL automation).
Knowledge of Unix/Linux for log analysis and scripting.
Understanding of CI/CD pipelines for data engineering.
Banking/Financial services domain experience (Credit Risk, Regulatory Reporting, BASEL, IFRS)
Experience working in regulatory or data-intensive environments.

Other Requirements

Required Skills and Experience

Strong hands-on experience in ETL/Data Warehouse Testing.
Expertise in source‑to‑target mapping (STTM) validation, data reconciliation, and data profiling.
Advanced SQL skills (Joins, subqueries, window functions, aggregations).
Solid experience with ETL workflows/pipelines, mappings, and transformations.
Working knowledge of job schedulers like Control-M, IBM Tivoli, etc., for job monitoring, and troubleshooting.
Familiarity with data modelling concepts (star schema, snowflake, SCDs).
Experience with defect management tools such as JIRA, Azure DevOps, or HP ALM.
Ability to analyse complex data flows and identify gaps or inconsistencies.
Strong communication skills and ability to work with cross‑functional teams.
The engineer is expected to operate effectively within Agile/Scrum teams, contributing to iterative delivery of data‑warehouse and ETL capabilities.

Preferred / Nice‑to‑Have

Experience with automation frameworks for ETL testing (Python, SQL automation).
Knowledge of Unix/Linux for log analysis and scripting.
Understanding of CI/CD pipelines for data engineering.
Banking/Financial services domain experience (Credit Risk, Regulatory Reporting, BASEL, IFRS)
Experience working in regulatory or data-intensive environments.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Summary

Role Overview

Key Responsibilities

Key Responsibilities

Analyse business requirements, data mappings, and ETL design documents to define comprehensive test strategies for data‑warehouse solutions.
Develop and execute functional, integration, system, and regression test cases for ETL workflows.
Validate data transformations, aggregations, and load processes using advanced SQL queries.
Perform source‑to‑target data validation, data profiling, and reconciliation across large datasets.
Perform data load using Informatica PowerCenter & BMC Control-M.
Monitor, validate and troubleshoot Control-M scheduled jobs, dependencies, and batch execution flows.
Log, track, and retest defects using tools like Zephyr & JIRA.
Closely collaborate with Developers, Business analysts, Data Analysts, and Business stakeholders to clarify requirements, resolve data issues, and ensure alignment with business expectations.
Build and maintain test automation frameworks for repetitive ETL validation tasks (Python, shell scripting, or SQL‑based automation).
Work with Git Version control, performing activities such as branch creation, pulling, pushing , merging code and conflict resolution.
Document test results, create defect reports, and maintain traceability across requirements, test cases, and execution cycles.
Support UAT cycles, production validation, and post‑deployment checks.

Skill Requirements

Required Skills and Experience

Strong hands-on experience in ETL/Data Warehouse Testing.
Expertise in source‑to‑target mapping (STTM) validation, data reconciliation, and data profiling.
Advanced SQL skills (Joins, subqueries, window functions, aggregations).
Solid experience with ETL workflows/pipelines, mappings, and transformations.
Working knowledge of job schedulers like Control-M, IBM Tivoli, etc., for job monitoring, and troubleshooting.
Familiarity with data modelling concepts (star schema, snowflake, SCDs).
Experience with defect management tools such as JIRA, Azure DevOps, or HP ALM.
Ability to analyse complex data flows and identify gaps or inconsistencies.
Strong communication skills and ability to work with cross‑functional teams.
The engineer is expected to operate effectively within Agile/Scrum teams, contributing to iterative delivery of data‑warehouse and ETL capabilities.

Preferred / Nice‑to‑Have

Experience with automation frameworks for ETL testing (Python, SQL automation).
Knowledge of Unix/Linux for log analysis and scripting.
Understanding of CI/CD pipelines for data engineering.
Banking/Financial services domain experience (Credit Risk, Regulatory Reporting, BASEL, IFRS)
Experience working in regulatory or data-intensive environments.

Other Requirements

Required Skills and Experience

Strong hands-on experience in ETL/Data Warehouse Testing.
Expertise in source‑to‑target mapping (STTM) validation, data reconciliation, and data profiling.
Advanced SQL skills (Joins, subqueries, window functions, aggregations).
Solid experience with ETL workflows/pipelines, mappings, and transformations.
Working knowledge of job schedulers like Control-M, IBM Tivoli, etc., for job monitoring, and troubleshooting.
Familiarity with data modelling concepts (star schema, snowflake, SCDs).
Experience with defect management tools such as JIRA, Azure DevOps, or HP ALM.
Ability to analyse complex data flows and identify gaps or inconsistencies.
Strong communication skills and ability to work with cross‑functional teams.
The engineer is expected to operate effectively within Agile/Scrum teams, contributing to iterative delivery of data‑warehouse and ETL capabilities.

Preferred / Nice‑to‑Have

Experience with automation frameworks for ETL testing (Python, SQL automation).
Knowledge of Unix/Linux for log analysis and scripting.
Understanding of CI/CD pipelines for data engineering.
Banking/Financial services domain experience (Credit Risk, Regulatory Reporting, BASEL, IFRS)
Experience working in regulatory or data-intensive environments.