ETL Tester

Q1 Technologies, Inc.
Charlotte, NC, United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours

Tech stack

Data Analysis CA Workload Automation Ae Extract Transform Load (ETL) Data Transformation Data Warehousing Apache Hadoop Hadoop Distributed File System Apache Hive Metadata Oracle (Applications) Shell Script PL-SQL
+5 more
SQL Databases Data Processing Apache Spark Test Scripts Apache Kafka

Requirements

Must Have Technical/Functional Skills Primary Skill: ETL testing Secondary: ETL Testing, Autosys, SQL, Shell Scripting, Exposure to IDMC/Informatica Experience: Minimum 10 years

Roles & Responsibilities

  • Analyze data mapping documents and business requirements to design comprehensive test plans and cases.
  • Perform source-to-target data reconciliation, check data loading, and ensure transformation rules are applied correctly.
  • Write complex SQL scripts for validation (count, data completeness, data consistency, data truncation).
  • Identify, log, and track data defects using tools like JIRA or Client ALM or Octane.
  • Automate test scripts and validate data volume, performance, and scalability.
  • Expert-level knowledge of SQL for data analysis.
  • Experience with tools such as Informatica and IDMC.
  • Understanding of data warehouse concepts and architectures (e.g., star/snowflake schema).
  • Familiarity with Hadoop or Spark is often preferred.
  • Validate HiveQL, HDFS file structures, and data processing within the Hadoop cluster.
  • Strong analytical and troubleshooting skills.
  • Excellent communication for collaborating with developers and stakeholders.
  • Knowledge in Metadata dependent ETL process and batch/job framework Tools & Skills: IDMC, Informatica Power center, Hive, Kafka Knowledge SQL, Oracle, PL/SQL Domain: Banking knowledge, Payments knowledge preferred. Concept: Data Warehousing, Data Transformation, ETL/ELT, Data Quality.

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