ETL Tester

Overview

Remote
Up to $90,000
Full Time

Skills

ETL
TEST
TESTER

Job Details

  • The ETL Cloud Tester is responsible for validating end-to-end data pipelines, ETL/ELT workflows, cloud-based data integration processes, and data quality across enterprise data platforms. The role ensures that data ingest, transformation, storage, and consumption layers meet functional, performance, and compliance requirements. The tester collaborates closely with data engineers, cloud architects, and business analysts to ensure accurate, reliable, secure, and high-performing data solutions.
  • Develop, execute, and maintain end-to-end test cases for ETL/ELT jobs across cloud platforms (Azure Data Factory, AWS Glue, Google Cloud Platform Dataflow, Informatica Cloud, Talend, DBT, etc.).
  • Validate data mapping, data transformations, cleansing logic, aggregations, and business rules.
  • Perform source-to-target data validation and extensive data profiling.
  • Conduct schema validation, referential integrity validation, and metadata testing.
  • Test incremental and full data loads, change data capture (CDC), and batch/streaming pipelines
  • Strong SQL skills (complex joins, window functions, profiling queries).
  • Expertise in ETL/ELT tools and cloud data integration services
  • Hands-on experience with one or more cloud ecosystems (Azure/AWS/Google Cloud Platform).
  • Experience testing data warehouses: Snowflake, Redshift, BigQuery, Synapse.
  • Understanding of data modeling (Star schema, OLAP/OLTP concepts).
  • Proficiency with Python or PySpark for validation scripting.
  • Experience with CI/CD pipelines for automated testing.
  • Familiarity with API testing (Postman, REST/JSON pipelines).
  • Lead ETL testing efforts for large cloud data modernization initiatives. Design enterprise-wide test strategies & automation frameworks. Validate performance, scalability, and cost efficiency of pipelines. Conduct root-cause analysis for data discrepancies across environments. Oversee data quality rule development and validation lifecycle. Drive automation adoption (Python, PySpark, custom validation utilities). Ensure compliance with regulatory requirements (HIPAA, HITRUST, SOC2). Define CI/CD-based automated regression suites for cloud pipelines.
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