Job Responsibilities :
SQL Development :
- - Write medium to complex SQL queries to support data extraction, transformation, and analysis.
- - Optimize SQL queries for performance and maintainability.PySpark/Spark Development :
- - Develop, test, and deploy data processing applications using PySpark or Spark with Scala.
- - Implement and maintain ETL/ELT data pipelines, ensuring efficient data processing and integration.ETL/ Data Engineering Pipeline Design :
Cloud Technology Utilization :
- - Leverage cloud platforms (AWS, Azure, Google Cloud, etc.) to build scalable and efficient data solutions.
- - Work with cloud
- -based data storage solutions (e.g., S3, Google Cloud Storage) and compute environments.
Debugging and Issue Resolution :
- - Debug data processing issues and resolve challenges independently as an individual contributor.
- - Troubleshoot and optimize existing pipelines to improve reliability and performance.Continuous Integration and Continuous Deployment (CI/CD) :
Continuous Integration and Continuous Deployment (CI/CD) :
- - Implement and manage CI/CD pipelines for automated testing, deployment, and monitoring.
- - Ensure code quality and best practices through version control (e.g., Git) and CI/CD tools.
Desired Skills :
Data Modeling :
- - Design and maintain logical and physical data models to support business requirements.
- - Ensure data models are optimized for performance, scalability, and ease of use.Airflow :
Airflow :
- - Use Apache Airflow to orchestrate and schedule complex data workflows.
- - Develop and maintain DAGs for task automation and monitoring.
Kafka :
- - Utilize Apache Kafka for building real-time data streaming pipelines.
- - Integrate Kafka with other systems for event-driven data processing.Big Data Technologies :
Big Data Technologies :
- - Experience with big data processing frameworks such as Hive, EMR, and Databricks.
- - Familiarity with data storage solutions like Snowflake for analytics and reporting.Data Lakes and Data Warehousing :
Data Lakes and Data Warehousing :
- - Work with data lake solutions and data warehouses for storing and managing large datasets.
- - Implement data lake architectures for scalable data ingestion and processing.
Technical Knowledge and Skills Required :
- - Strong hands-on experience with PySpark or Spark with Scala for big data processing.
- - Proficient in writing complex SQL queries and optimizing them for performance.
- - Understanding of ETL/ELT design principles and best practices.
- - Basic to intermediate knowledge of CI/CD processes and tools.
- - Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their data -related services.
- - Ability to work independently and resolve issues effectively.
Soft Skills Required :
- - Strong analytical and problem‑solving skills.
- - Excellent communication and collaboration abilities.
- - Ability to manage multiple tasks and projects efficiently.
- - A proactive attitude towards learning new technologies and improving existing skills.
Work Experience :
- - 5+ years of relevant experience in data engineering, focusing on big data processing and cloud technologies.
- - Experience working with data engineering tools and frameworks like Airflow, Kafka, Hive, EMR, Databricks, and Snowflake.Education :
Education :
- - Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field.
- - A Master's degree is a plus.