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Data Engineer
4 weeks ago
Key Responsibilities
- Develop & Maintain Data Pipelines – Design, build, and optimize scalable ETL/ELT workflows to collect, transform, and store structured and unstructured data
- Database & Query Optimization – Optimize SQL queries, ensure efficient data storage, and improve query performance for better system efficiency
- Data Quality & Validation – Implement data quality checks and validation mechanisms to ensure data integrity and consistency across sources
- Cloud & Data Warehousing – Work with Google Cloud Platform (GCP) (preferred) and data warehousing tools like BigQuery to manage large-scale datasets
- Data Scraping & Processing – Utilize data scraping techniques to extract valuable insights from various web sources
- Automation & Scheduling – Utilize Apache Airflow or similar ETL tools to automate data workflows and ensure smooth data processing
- Collaboration – Work closely with data analysts, data scientists, and software engineers to understand data needs and optimize data architecture
- Troubleshooting & Performance Tuning – Diagnose and resolve issues related to data pipelines, databases, and infrastructure
Requirements & Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field
- 1-2 years of experience in data engineering or a similar role
- Strong proficiency in SQL and experience with relational databases (e.g., MySQL, PostgreSQL, etc.)
- Hands-on experience with Python for data processing and automation
- Knowledge of ETL processes and familiarity with Apache Airflow or similar workflow orchestration tools
- Experience with data warehousing and cloud platforms, especially Google Cloud Platform (GCP) and BigQuery
- Understanding of data modeling concepts and techniques
- Knowledge of data scraping techniques and working with APIs for data extraction
- Strong problem-solving and analytical skills to troubleshoot and optimize data systems
- Excellent communication and collaboration skills to work in a team-driven environment
Nice-to-Have Skills (Preferred but Not Mandatory)
- Familiarity with real-time data processing frameworks like Apache Kafka or Spark Streaming
- Exposure to CI/CD pipelines and DevOps for data engineering workflows
- Knowledge of Docker/Kubernetes for containerized data processing
- Knowledge of Frontend/Backend scripting frameworks is an advantage