SDET Tester at HAN Staffing Chicago, IL

HAN Staffing; Chicago (IL); Presencial; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

HAN Staffing in Chicago, IL is seeking an SDET Tester to drive data engineering testing efforts. The role focuses on QA automation for data pipelines, validating ETL processes, and ensuring data quality across platforms.

The ideal candidate has 5+ years in data QA, strong Python skills with PyTest, and expertise in SQL. Experience with Azure Databricks and Azure data services is highly valued.

Qualificações

• 5+ years of QA automation for data engineering, ETL, or similar domains.
• Experience with data testing tools like Great Expectations.
• Strong Python for test automation with PyTest or unittest.
• Proficiency in SQL for data validation and querying.
• Hands-on experience with testing large-scale data pipelines on cloud platforms.
• Familiarity with Azure Data Cloud services (ADF, Synapse, ADLS Gen 2, CosmosDB).

Conhecimentos

QA automation

Data testing

Python

PyTest

SQL

Azure Databricks

Ferramentas

Great Expectations

Azure Data Cloud

ADF

Synapse

ADLS Gen 2

CosmosDB

Kafka

Azure Monitor

Azure Log Analytics

Parquet

Delta Lake

Descrição da oferta de emprego

Job ID: 24389559 Reference Number: 24-01245 Title: SDET Tester Location: Chicago, IL, 08830 Posted Date: 2024-12-16 Company: HAN Staffing Role: SDET Tester

Data Engineering Testing

• 5+ years of experience in QA automation for data engineering, ETL, or similar domains.
• Experience with data testing tools and frameworks such as Great Expectations or similar.
• Strong expertise in Python for test automation, with experience in libraries like PyTest or unittest.
• Proficiency in SQL for data validation and querying.
• Hands-on experience with testing large-scale data pipelines in data platforms like Azure Databricks (preferred) or SnowflakeAzure Ecosystem Expertise:
• Experience with Azure Data Cloud - ADF, Synapse, ADLS Gen 2, CosmosDB
• Familiarity with Azure Event Hub or Kafka for validating streaming data pipelines.
• Understanding of Azure Blob Storage and Azure Data Lake for data validation tasks.
• Exposure to monitoring tools like Azure Monitor and Azure Log Analytics for analyzing test results and pipeline performance.
• Familiarity with data security and governance practices for Azure Databricks
• Familiarity with modern data lakehouse data formats - delta table, parquet
• Knowledge of data modeling concepts such as Star Schema, Data Vault and Medallion Architecture