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.