Key Responsibilities:

• Design, develop, and maintain scalable data pipelines and ETL processes.
• Implement and optimize data storage solutions, including data lakes and warehouses.
• Ensure data integrity, security, and reliability across all platforms.
• Collaborate with data analysts, data scientists, and business teams to provide reliable data solutions.
• Monitor and improve data performance, ensuring efficient data processing and accessibility.
• Work with cloud-based data infrastructure (AWS, GCP, or Azure) to enhance data engineering capabilities.
• Automate data workflows and implement best practices for data governance and compliance.
• Troubleshoot and resolve data-related issues, ensuring smooth operations.
• Stay updated with emerging data technologies and industry trends to drive continuous improvement.

Qualifications and Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related field.
• 5+ years of experience in data engineering, big data processing, or related fields.
• Strong proficiency in SQL, Python, and big data technologies (Spark, Hadoop, Kafka, etc.).
• Experience with cloud-based data platforms (AWS Redshift, Google BigQuery, Azure Synapse, etc.).
• Knowledge of database systems (SQL and NoSQL), data modeling, and data warehousing concepts.
• Hands-on experience with ETL tools and workflow orchestration (Airflow, dbt, etc.).
• Familiarity with containerization and DevOps tools (Docker, Kubernetes, CI/CD pipelines).
• Strong analytical and problem-solving skills with attention to detail.
• Excellent communication and teamwork skills to collaborate with cross-functional teams.

Please refer to job description.