Data Engineer - ETL/PySpark

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 :

- Design and implement robust ETL pipelines to support data ingestion, transformation, and loading.

- Collaborate with data architects and engineers to ensure seamless data flow and integration across systems.

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) :

- 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 :

- 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 :

- 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 :

- 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 :

- Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field.

- A Master's degree is a plus.