
ETL Specialist
4 weeks ago
Key Responsibilities:
- ETL Pipeline Development: Design, develop, and maintain efficient ETL workflows using Python to extract, transform, and load data into structured data warehouses.
- Data Pipeline Optimization: Monitor and optimize data pipeline performance, ensuring scalability and reliability in handling large data volumes.
- Linux Server Management: Work in a Linux-based environment, executing command-line operations, managing processes, and troubleshooting system performance issues.
- File Handling & Storage Management: Efficiently manage data files in Amazon S3, ensuring proper storage organization, retrieval, and archiving of data.
- Disk Space & Warehouse Balancing: Proactively monitor and manage disk space usage, preventing storage bottlenecks and ensuring warehouse efficiency.
- Error Handling & Logging: Implement robust error-handling mechanisms and logging systems to monitor data pipeline health.
- Automation & Scheduling: Automate ETL processes using cron jobs, Airflow, or other workflow orchestration tools.
- Data Quality & Validation: Ensure data integrity and consistency by implementing validation checks and reconciliation processes.
- Security & Compliance: Follow best practices in data security, access control, and compliance while handling sensitive data.
- Collaboration with Teams: Work closely with data engineers, analysts, and product teams to align data processing with business needs.
Skills Required:
- Proficiency in Python: Strong hands-on experience in writing Python scripts for ETL processes.
- Linux Expertise: Experience working with Linux servers, command-line operations, and system performance tuning.
- Cloud Storage Management: Hands-on experience with Amazon S3, including handling file storage, retrieval, and lifecycle policies.
- Data Pipeline Management: Experience with ETL frameworks, data pipeline automation, and workflow scheduling (e.g., Apache Airflow, Luigi, or Prefect).
- SQL & Database Handling: Strong SQL skills for data extraction, transformation, and loading into relational databases and data warehouses.
- Disk Space & Storage Optimization: Ability to manage disk space efficiently, balancing usage across different systems.
- Error Handling & Debugging: Strong problem-solving skills to troubleshoot ETL failures, debug logs, and resolve data inconsistencies.
Nice to Have:
- Experience with cloud data warehouses (e.g., Snowflake, Redshift, BigQuery).
- Knowledge of message queues (Kafka, RabbitMQ) for data streaming.
- Familiarity with containerization tools (Docker, Kubernetes) for deployment.
- Exposure to infrastructure automation tools (Terraform, Ansible).
Qualifications:
- Bachelor's degree in Computer Science, Data Engineering, or a related field.
- 3+ years of experience in ETL development, data pipeline management, or backend data engineering.
- Strong analytical mindset and ability to handle large-scale data processing efficiently.
- Ability to work independently in a fast-paced, product-driven environment.
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