Data Engineer

Description: Design, develop, and maintain scalable ETL / ELT pipelines.
Build and optimize data workflows on Databricks and Azure Cloud.
Develop high-performance SQL transformations and data models.
Work with large-scale structured and semi-structured datasets.
Improve data quality, reliability, and pipeline performance.
Support migration and modernization of enterprise data platforms.
Collaborate with stakeholders to understand business and technical requirements.
Troubleshoot production issues and optimize existing data processes.
Participate in architecture and technical solution discussions.
Mentor junior engineers and share best practices with the team.
Qualifications.
At least 5 years of experience in Data Engineering or related roles.
Strong expertise in SQL and ETL development.
Hands-on experience with Azure Cloud and Databricks.
Experience working with enterprise-scale data platforms.
Strong understanding of Data Warehouse and Data Lake concepts.
Experience with PySpark / Spark is highly preferred.
Familiarity with cloud-based data architecture and optimization.
Experience in performance tuning and troubleshooting ETL jobs.
Understanding of CI/CD and version control (Git).
Good problem-solving and analytical skills.
Able to work independently and communicate effectively with teams.
Job skills required: Cloud Computing
Job skills preferred: Architecture, Git, SQL, ETL