Python Data Engineer

We are seeking a seasoned Python Data Engineer with a strong background in distributed computing and cloud data warehousing. In this role, you will design and build scalable data pipelines that process massive datasets, turning raw data into actionable insights.

As a mid-senior level engineer, you will be expected to architect efficient ETL workflows, optimize data processing scripts, and maintain our core data infrastructure on AWS and Snowflake.

Core Languages Expert-level Python (specifically for data engineering patterns).
Data Libraries Advanced proficiency in Pandas, NumPy, and PySpark.Cloud Platform 2+ years of hands-on experience with AWS services.Data Warehouse Deep understanding of Snowflake (Snowpipe, Tasks, Streams, and Roles).SQL Mastery of complex SQL queries and window functions.Tools & DevOps Experience with Git, Docker, and Airflow (or similar orchestration tools)Technical Skills Required:

Must Have:

• Strong experience in .NET Core / ASP.NET / C#
• Experience with Web API development and Microservices architecture
• Hands-on experience with AWS Cloud services (EC2, Lambda, S3, RDS, SNS, SQS, CloudWatch, etc.)
• Strong understanding of OOPs concepts, Design Patterns, and SOLID principles
• Experience with Entity Framework / LINQ / SQL Server / DynamoDB
• Exposure to CI/CD pipelines and Git-based source control

Good to Have:

• Experience with Docker / Kubernetes
• Familiarity with Serverless applications (AWS Lambda, API Gateway)
• Knowledge of Angular / React / Vue.js (for full-stack roles)
• Basic understanding of Terraform / CloudFormation for infrastructure automation

Key Responsibilities
Pipeline Development: Design, develop, and deploy end-to-end ETL/ELT pipelines using Python and PySpark to handle structured and unstructured data.

Data Processing: Utilize Pandas and NumPy for complex data manipulation, statistical analysis, and data validation tasks.

Cloud Architecture: Architect and manage data flows within the AWS ecosystem (S3, Glue, Lambda, EMR).

Data Warehousing: Optimize and manage large-scale data storage and retrieval in Snowflake, ensuring high performance through effective clustering and partitioning.

Performance Optimization: Identify bottlenecks in data processing jobs and refactor code for maximum efficiency and cost-effectiveness.

Code Quality: Maintain high standards of code through unit testing, documentation, and participation in peer code reviews.

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