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Must Have Technical/Functional Skills
We are looking for a skilled PySpark Data Engineer with strong hands-on experience in PySpark and Python to design, build, and optimize scalable data processing pipelines. The ideal candidate will have practical experience working with distributed data processing and a solid foundation in writing efficient, production-grade Python code
Required Technical Skills
Strong hands-on experience in PySpark (Spark SQL, DataFrame API)
Advanced proficiency in Python (data processing, performance tuning, modular coding)
Solid understanding of ETL design patterns and data pipeline architecture
Good working knowledge of SQL for data transformation and analysis
Experience with data processing in distributed environments
Preferred Skills (Good to Have)
Experience with cloud platforms (AWS preferred – S3, Glue, EMR or equivalent services)
Familiarity with workflow orchestration tools such as Airflow or similar schedulers
Exposure to data warehousing concepts (e.g., Snowflake or similar platforms)
Knowledge of code versioning (Git) and CI/CD practices
Experience
3–8 years of experience in Data Engineering / PySpark development
Proven hands-on project experience in PySpark + Python
Roles & Responsibilities
Design, develop, and maintain ETL/ELT pipelines using PySpark
Write optimized and scalable PySpark transformations using DataFrames and Spark SQL
Develop reusable and efficient Python-based data processing components
Ensure data quality, integrity, and performance across pipelines
Perform debugging, performance tuning, and optimization of PySpark jobs
Collaborate with cross-functional teams (Data Analysts, Architects, DevOps)
Contribute to CI/CD pipelines and deployment workflows for data applications
Monitor and troubleshoot data workloads in production environments
The minimum salary is $100K and the max salary is $120K.
$100K – $120K/yr (Employer provided)
$110K
/yr Median
Irving, TX
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