Python Developers
Description:
Job Description
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Role: Python Developers
Location: Mclean, VA
Exp: 8 Years
Requirements
We are seeking a highly skilled and motivated Python Developer with strong expertise in PySpark and AWS to join our data engineering team. The ideal candidate will be responsible for building scalable data pipelines, transforming large volumes of data, and deploying data solutions in cloud environments. You will collaborate with cross-functional teams to design, develop, and implement high-performance, reliable, and scalable data processing systems.
Key Responsibilities:
Design, develop, and maintain efficient, reusable, and reliable Python code.
Develop scalable data processing pipelines using PySpark for structured and semi-structured data.
Build and automate data workflows and ETL pipelines using AWS services such as S3, Glue, Lambda, EMR, and Step Functions.
Optimize data processing for performance, scalability, and reliability.
Participate in architecture design discussions and contribute to technical decision-making.
Integrate with data sources like RDBMS, NoSQL, and REST APIs.
Implement data quality checks, monitoring, and logging for production pipelines.
Work closely with data analysts, architects, and DevOps teams to ensure seamless data flow and integration.
Perform unit testing, debugging, and performance tuning of code.
Maintain documentation for all developed components and processes.
Required Skills and Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
4+ years of experience in Python programming for data engineering or backend development.
Strong hands-on experience with PySpark (RDD, DataFrame APIs, Spark SQL, performance tuning).
Proficient in using AWS services like S3, Glue, Lambda, EMR, Athena, and CloudWatch.
Good understanding of distributed computing and parallel data processing.
Experience working with large-scale datasets and batch/streaming data pipelines.
Familiarity with SQL and data modeling concepts.
Knowledge of CI/CD tools and source control (e.g., Git, Jenkins).
Solid understanding of software engineering best practices and Agile methodologies.
Preferred Qualifications:
AWS certification (e.g., AWS Certified Developer or Data Analytics Specialty).
Experience with containerization (Docker) and orchestration tools (Kubernetes).
Familiarity with data lake and data warehouse concepts (e.g., Redshift, Snowflake).
Exposure to Apache Airflow or other workflow orchestration tools.
We are seeking a highly skilled and motivated Python Developer with strong expertise in PySpark and AWS to join our data engineering team. The ideal candidate will be responsible for building scalable data pipelines, transforming large volumes of data, and deploying data solutions in cloud environments. You will collaborate with cross-functional teams to design, develop, and implement high-performance, reliable, and scalable data processing systems.
Key Responsibilities:
Design, develop, and maintain efficient, reusable, and reliable Python code.
Develop scalable data processing pipelines using PySpark for structured and semi-structured data.
Build and automate data workflows and ETL pipelines using AWS services such as S3, Glue, Lambda, EMR, and Step Functions.
Optimize data processing for performance, scalability, and reliability.
Participate in architecture design discussions and contribute to technical decision-making.
Integrate with data sources like RDBMS, NoSQL, and REST APIs.
Implement data quality checks, monitoring, and logging for production pipelines.
Work closely with data analysts, architects, and DevOps teams to ensure seamless data flow and integration.
Perform unit testing, debugging, and performance tuning of code.
Maintain documentation for all developed components and processes.
Required Skills and Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
4+ years of experience in Python programming for data engineering or backend development.
Strong hands-on experience with PySpark (RDD, DataFrame APIs, Spark SQL, performance tuning).
Proficient in using AWS services like S3, Glue, Lambda, EMR, Athena, and CloudWatch.
Good understanding of distributed computing and parallel data processing.
Experience working with large-scale datasets and batch/streaming data pipelines.
Familiarity with SQL and data modeling concepts.
Knowledge of CI/CD tools and source control (e.g., Git, Jenkins).
Solid understanding of software engineering best practices and Agile methodologies.
Preferred Qualifications:
AWS certification (e.g., AWS Certified Developer or Data Analytics Specialty).
Experience with containerization (Docker) and orchestration tools (Kubernetes).
Familiarity with data lake and data warehouse concepts (e.g., Redshift, Snowflake).
Exposure to Apache Airflow or other workflow orchestration tools.
Benefits
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Full-time