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Data Quality Engineer with Python//W2/W2Only Max$40-43// ( New York City)Hybrid

This role is for a Data Quality Engineer with Python expertise, hybrid in New York City, lasting 12+ months. Pay is $40-43/hour. Key skills include data validation, ETL testing, automation, and experience with Python frameworks like Pandas and PyTest.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
344
🗓️ - Date discovered
February 21, 2025
🕒 - Project duration
More than 6 months
🏝️ - Location type
Hybrid
📄 - Contract type
W2 Contractor
🔒 - Security clearance
Unknown
📍 - Location detailed
New York City Metropolitan Area
🧠 - Skills detailed
#"ETL (Extract #Transform #Load)" #DevOps #Quality Assurance #Automation #Python #Anomaly Detection #Data Pipeline #Data Governance #Data Engineering #Pandas #Pytest #Data Profiling #Data Quality #Data Accuracy
Role description
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I have an urgent role for Data Quality Engineer with Python//W2

If you are interested in, please share your updated resume

Update from the client side This role is hybrid in New York City Twice in a month

Location : ( New York City ) EST Timings(Hybrid)

Interview process: Phone + Skype

Duration: 12+ Months(C2H)

W2 Only Max

Job Description for Data Quality Engineer with Python

Job Summary:

We are seeking a Data Quality Engineer with Python expertise to ensure the integrity, accuracy, and reliability of our data pipelines and systems. The ideal candidate will have experience in data validation, ETL testing, and automation using Python-based frameworks. You will work closely with data engineers, analysts, and business teams to implement data quality processes, resolve discrepancies, and improve overall data governance. If you are passionate about data accuracy and automation, we want you on our team!

  1. Data Validation & Quality Assurance
    • Develop and implement data validation frameworks to ensure data accuracy, completeness, and consistency.
    • Perform data profiling, anomaly detection, and root cause analysis to identify data quality issues.
    • Design and execute test cases and scripts for data validation using Python.

  2. Automation & Testing
    • Automate data quality checks and validation processes using Python-based frameworks (e.g., Great Expectations, Pandas, PyTest).
    • Develop ETL testing scripts to validate data pipelines, transformations, and ingestion processes.
    • Work with DevOps teams to integrate data quality tests into CI/CD pipelines.