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ETL QA - Lead @ Iris Software

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 ETL QA - Lead

Job Description

Key Responsibilities:
  • Data Testing Strategy & Execution: Design, develop, and execute comprehensive test plans and test cases for data-centric applications, ETL processes, data warehouses, data lakes, and reporting solutions
  • SQL-Driven Validation: Utilize advanced SQL queries to perform complex data validation, data reconciliation, data integrity checks, and data quality assurance across various financial data sources
  • ETL Testing: Conduct thorough testing of ETL (Extract, Transform, Load) processes, ensuring data is accurately extracted, transformed according to business rules, and loaded correctly into target systems
  • Data Quality Assurance: Implement and monitor data quality checks, identify data discrepancies, anomalies, and inconsistencies, and work with development and business teams to resolve issues
  • Performance Testing (Data Focus): Contribute to performance testing efforts for data pipelines and database operations, ensuring optimal query and data load performance
  • Test Data Management: Create and manage robust test data sets for various testing phases, including positive, negative, and edge case scenarios
  • Defect Management: Identify, document, track, and re-test defects in data, collaborating closely with development and data engineering teams for timely resolution
  • Documentation & Reporting: Maintain clear and concise documentation of test plans, test cases, test results, and data quality reports
  • Provide regular status updates to stakeholders
  • Collaboration: Work effectively with business analysts, data architects, data engineers, and project managers to understand data flows, business requirements, and ensure data quality standards are met
  • Process Improvement: Proactively identify opportunities for process improvements in data testing methodologies and tools
  • Global Team Collaboration: Provide consistent overlap with EST working hours (until noon EST) to facilitate effective communication and collaboration with US-based teams.
    Required Skills & Experience: Experience:
    • 8-10 years of hands-on experience in Data Quality Assurance, Data Testing, or ETL Testing roles.
    • SQL Expertise:
    • Advanced proficiency in SQL: Ability to write complex queries, subqueries, analytical functions (Window functions), CTEs, and stored procedures for data validation, reconciliation, and analysis.
    • Experience with various SQL databases (e.g., SQL Server, Oracle, PostgreSQL, MySQL, Snowflake, BigQuery).
    • Strong understanding of database concepts: normalization, indexing, primary/foreign keys, and data types.
    • Data Testing Methodologies: Solid understanding of data warehousing concepts, ETL processes, and various data testing strategies (e.g., source-to-target mapping validation, data transformation testing, data load testing, data completeness, data accuracy).
    • Domain Expertise:o Strong understanding and proven experience in Risk and Finance IT domain: Familiarity with financial data (e.g., trading data, market data, risk metrics, accounting data, regulatory reporting).
    • Knowledge of financial products, regulations, and risk management concepts.
    • Analytical & Problem-Solving Skills: Excellent ability to analyze complex data sets, identify root causes of data issues, and propose effective solutions.
    • Communication: Strong verbal and written communication skills to articulate data issues and collaborate with diverse teams.
    Mandatory Competencies
    • ETL - ETL - Tester
    • QA/QE - QA Automation - ETL Testing
    • Database - PostgreSQL - PostgreSQL
    • Beh - Communication
    • Database - Sql Server - SQL Packages.

    Job Classification

    Industry: IT Services & Consulting
    Functional Area / Department: Engineering - Software & QA
    Role Category: DBA / Data warehousing
    Role: Data warehouse Architect / Consultant
    Employement Type: Full time

    Contact Details:

    Company: Iris Software
    Location(s): Noida, Gurugram

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    Keyskills:   sql etl testing data warehousing concepts etl etl process snowflake oracle data testing dbms warehouse sql server data quality postgresql qe data reconciliation database creation quality assurance qa automation mysql bigquery finance

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