QA Test Engineer ETL

Accord Technologies Inc; Seattle (WA); On-site; USD 100,000 - 130,000; Full time

Job summary

A technology company is seeking a QA/Test Engineer in Seattle to build and execute test strategies for data ingestion and ETL pipelines. Your experience of 8-10 years in QA/test engineering will be essential as you ensure high data quality through automated tests and frameworks. Strong skills in Python and CI/CD processes are required, with experience on AWS preferred. This role offers the opportunity to work in a collaborative Agile environment focusing on data quality and pipeline validation.

Qualifications

• 8-10 years of experience in QA/test engineering focused on data pipelines.
• Strong proficiency in Python for test automation.
• Hands-on experience testing data pipelines on AWS.

Responsibilities

• Design and implement a reusable test framework for data pipelines.
• Write and maintain unit tests for transformation logic.
• Develop integration tests validating end-to-end data flow.

Skills

ETL testing

data quality testing

SQL (strong)

Python

CI/CD (Jenkins, GitHub Actions)

API testing

security testing

Tools

CloudWatch

PySpark

AWS (Glue, Spark, S3, Athena, Redshift)

Job description

Role

QA/Test Engineer – Seattle, WA • Position type: C2C • Visa: USC/GC

Mandatory Skills

• ETL testing, data quality testing
• Workflow testing (Step Functions, Airflow)
• SQL (strong), Python, PySpark
• Logging and monitoring using CloudWatch
• CI/CD (Jenkins, GitHub Actions, CodePipeline)
• API testing and microservices validation
• Security testing (IAM, encryption, policies)

Role Summary

The QA/Test Engineer is responsible for building and executing comprehensive test strategies for all ingestion and ETL pipelines. This role ensures that every pipeline meets the 98–99% data quality pass rate required by the SOW before production promotion, and that automated tests are embedded in the CI/CD process.

Key Responsibilities

• Design and implement a reusable test framework for unit, integration, and end-to-end testing of data ingestion and ETL pipelines.
• Write and maintain unit tests for individual transformation logic, connector behavior, and schema validation.
• Develop integration tests that validate end-to-end data flow from source ingestion through raw → curated → consumption layers.
• Build automated test suites that execute as part of the CI/CD pipeline, gating production deployments.
• Validate data quality check behavior: confirm that DQ rules (completeness, schema conformance, record counts, freshness) correctly trigger fail/alert actions.
• Perform staging environment validation using sample data provided by source owners before production promotion.
• Track and report test coverage, defect rates, and pipeline validation results for each milestone.
• Collaborate with Data/Cloud Engineers to define test cases, edge cases, and acceptance criteria for each data source.
• Support performance and load testing for streaming and high-volume batch sources where representative test data is available.
• Produce test artifacts, test plans, and validation reports as part of milestone documentation.

Required Skills & Qualifications

• 8-10 years of experience in QA/test engineering with a focus on data pipelines, ETL processes, or data platforms.
• Strong proficiency in Python for test automation and data validation scripting.
• Experience with data testing frameworks (e.g., Great Expectations, dbt tests, pytest, or custom frameworks).
• Hands-on experience testing data pipelines on AWS (Glue, Spark, S3, Athena, Redshift).
• Understanding of data quality dimensions: completeness, accuracy, consistency, freshness, schema conformance.
• Experience integrating automated tests into CI/CD pipelines (GitHub Actions, CodePipeline, Jenkins).
• Ability to write SQL queries for data validation and reconciliation.
• Familiarity with test data management strategies for sensitive or regulated data.
• Strong documentation skills for test plans, test cases, and validation reports.
• Experience working in Agile/Scrum teams with 2-week sprint cycles.

Preferred Skills

• Experience with performance/load testing for data pipelines.
• Familiarity with contract testing for APIs and schema registries.
• Knowledge of data observability tools (Monte Carlo, Datafold, or similar).

Domain: Aerospace preferred but not mandatory.