Key Responsibilities :
SQL Development :
- - Write medium-complexity SQL queries to extract, transform, and load data efficiently.
- - Optimize SQL queries for performance and scalability.
- - Collaborate with team members to understand data requirements and translate them into actionable SQL scripts.
PySpark/Spark Development :
- - Develop and maintain data processing scripts using PySpark or Spark with Scala.
- - Implement data transformation and ETL processes using Spark to handle large-scale data.
- - Optimize Spark jobs for performance and troubleshoot any issues that arise during processing.
Data Engineering Pipeline Design :
- - Design and implement ETL/data engineering pipelines to support business intelligence and analytics needs.
- - Ensure data pipelines are scalable, reliable, and maintainable.
- - Collaborate with the team to understand data flow requirements and implement appropriate solutions.
Cloud Technology Implementation :
- - Work with various cloud platforms (AWS, Azure, GCP) to build and manage data solutions.
- - Implement cloud-based data storage and processing solutions, ensuring security and compliance.
- - Utilize cloud services for data integration, orchestration, and processing.
Issue Resolution and Debugging :
- - Identify, diagnose, and resolve data-related issues and bugs independently.
- - Perform root cause analysis and implement fixes for any data discrepancies or processing failures.
- - Monitor data pipelines and systems to ensure continuous availability and reliability.
CI/CD Integration :
- - Implement and maintain basic to intermediate level CI/CD processes for data engineering projects.
- - Collaborate with DevOps teams to ensure smooth deployment and integration of data solutions.
- - Automate testing and deployment processes to improve development efficiency.
Desired Skills :
Data Modeling :
- - Experience in designing data models to support analytical and business requirements.
Airflow :
- - Experience with Apache Airflow for workflow scheduling and orchestration.
Kafka :
- - Knowledge of Kafka for real-time data streaming and integration.
EMR/Databricks :
- - Experience with AWS EMR or Databricks for big data processing and analytics.
Snowflake :
- - Familiarity with Snowflake for cloud-based data warehousing solutions.
Hive :
- - Understanding of Hive for querying and managing large datasets.
Qualifications :
- - Experience : 5+ years of experience in data engineering or related fields.
Technical Skills :
- - Strong proficiency in SQL and writing medium-complexity queries.
- - Hands-on experience with PySpark or Spark with Scala.
- - Understanding of ETL processes and data engineering pipeline design.
- - Basic to intermediate knowledge of CI/CD processes.
- - Experience with cloud technologies (AWS, Azure, GCP).
Problem-Solving :
- - Ability to troubleshoot and resolve data issues independently.
Communication :
- - Strong verbal and written communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders.
Education :
- - Bachelor's degree in Computer Science, Information Technology, or related field.