Data Engineer

Key Responsibilities:

• Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines and data architectures.
  
  
• Integrate data from multiple sources into centralized data warehouses or data lakes .
  
  
• Optimize data storage, transformation, and retrieval processes for performance and scalability.
  
  
• Collaborate with cross-functional teams (Data Science, Analytics, BI, and Engineering) to deliver data solutions.
  
  
• Implement data governance, quality, and security best practices.
  
  
• Automate data workflows and monitor data pipeline performance and reliability.
  
  
• Develop and maintain data models , schemas, and metadata documentation.
  
  
• Work with cloud data services (AWS, Azure, or GCP) to manage and deploy data solutions.
  
  
• Troubleshoot data issues, perform root cause analysis, and ensure high data integrity.
  
  
• Continuously evaluate new technologies to improve data engineering processes.
  
  

Required Skills & Qualifications:

• Bachelor’s or Master’s Degree in Computer Science, Information Technology, or related field.
  
  
• 5–7 years of hands-on experience in Data Engineering or a related role.
  
  
• Strong proficiency in SQL and experience with relational and non-relational databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
  
  
• Expertise in data pipeline tools such as Apache Airflow, Kafka, Spark, NiFi, or Talend .
  
  
• Strong programming skills in Python, Java, or Scala for data manipulation and automation.
  
  
• Experience with big data frameworks (Hadoop, Spark, Hive, HBase).
  
  
• Proven experience in cloud platforms (AWS Glue, Redshift, S3, Azure Data Factory, GCP BigQuery, etc.).
  
  
• Hands-on experience with data modeling , ETL design , and data warehouse architecture .
  
  
• Familiarity with DevOps , CI/CD pipelines , and containerization (Docker, Kubernetes) is a plus.
  
  
• Strong analytical and problem-solving skills with attention to detail.