Data Engineer

• Build, maintain, and document scalable data pipelines and data ingestion processes.
• Develop and support ETL/ELT workflows to ensure reliable and efficient data movement across systems.
• Assist in developing batch and real-time data processing solutions.
• Support the implementation and maintenance of modern data lake and lakehouse architectures.
• Write and optimize data transformation logic using PySpark and PL/SQL.
• Develop, monitor, and maintain Apache Airflow DAGs for workflow orchestration.
• Ensure data quality, accuracy, and consistency across data platforms.
• Collaborate with Data Scientists, BI Developers, Machine Learning Engineers, and Platform Teams to deliver data solutions.
• Participate in troubleshooting, performance tuning, and optimization of data pipelines.
• Follow data engineering best practices, coding standards, and version control processes.

Required Qualifications

• 2–4 years of experience in Data Engineering or a related field.
• Strong foundation in PySpark and PL/SQL.
• Hands-on experience with Apache Airflow and workflow orchestration.
• Proficiency with Git and version control best practices.
• Solid understanding of ETL/ELT processes and data modeling concepts.
• Exposure to cloud-based environments and big data platforms such as Databricks or similar technologies.
• Familiarity with data integration, transformation, and data quality practices.
• Strong analytical and problem-solving skills.
• Good communication and collaboration abilities.