Data Engineer+Python+Pyspark

Job Description: Data Engineer (PySpark + AWS Glue + Snowflake)

About the Role

We are seeking a highly skilled Data Engineer with expertise in PySpark, AWS Glue, and Snowflake to design, build, and optimize scalable data pipelines. The ideal candidate will have strong problem-solving skills, experience with cloud-based data platforms, and the ability to collaborate across teams to deliver high-quality data solutions.

Key Responsibilities

• Data Pipeline Development: Design, implement, and maintain ETL/ELT workflows using PySpark and AWS Glue.
• Snowflake Integration: Develop and optimize data models, schemas, and queries in Snowflake.
• Data Quality Assurance: Ensure accuracy, consistency, and reliability of data across systems.
• Performance Optimization: Tune PySpark jobs and Snowflake queries for efficiency.
• Collaboration: Work closely with data scientists, analysts, and business stakeholders to deliver actionable insights.
• Documentation: Maintain clear technical documentation for workflows, processes, and standards.

Required Skills

• PySpark: Strong experience in distributed data processing.
• AWS Glue: Hands-on expertise in building serverless ETL pipelines.
• Snowflake: Proficiency in data warehousing, query optimization, and performance tuning.
• SQL: Advanced query writing and optimization skills.
• Cloud Platforms: Familiarity with AWS ecosystem (S3, Lambda, IAM, CloudWatch).
• Version Control: Experience with Git/GitHub for collaborative development.

Preferred Qualifications

• Experience with Data Lake Architecture.
• Knowledge of CI/CD pipelines for data workflows.
• Exposure to Streaming Data (Kafka, Kinesis).
• Strong problem-solving and analytical skills.

Education & Experience

• Bachelors or Master’s degree in Computer Science, Information Technology, or related field.
• 3–6 years of experience in data engineering role.