Big data Engineer

Role Overview

two Junior-level Big Data Engineers

AWS, Python, Apache Airflow, and Big Data technologies

scalable data pipelines and workflows

Key Responsibilities

• Design, develop, and maintain ETL data pipelines using Apache Airflow.
• Build scalable data solutions on AWS, leveraging services such as:
• Amazon S3
• EC2
• AWS Lambda
• EMR
• AWS Glue
• Amazon Redshift
• Write clean, efficient, and maintainable Python code for data processing and automation.
• Work with Big Data frameworks such as Spark and Hadoop for large-scale data processing.
• Optimize workflows for performance, scalability, and cost efficiency.
• Collaborate with cross-functional teams to gather data requirements and deliver solutions.
• Ensure data quality, reliability, and security across pipelines and processes.
• Troubleshoot pipeline failures and resolve performance bottlenecks.
  

Required Qualifications

• 3–5 years of hands-on experience in Data Engineering roles.
• Strong experience with AWS cloud services for data engineering workloads.
• Proficiency in Python, including libraries such as Pandas and PySpark.
• Hands-on experience with Apache Airflow, including DAG creation and monitoring.
• Familiarity with Big Data technologies (Spark, Hadoop, or similar).
• Solid understanding of ETL processes and data modeling concepts.
• Strong analytical, problem-solving, and debugging skills.
• Excellent communication and collaboration abilities.
  

Preferred Qualifications

• Experience with CI/CD pipelines and DevOps practices.
• Knowledge of SQL and relational databases.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Exposure to monitoring and logging tools in cloud environments