Intermediate Data Engineer

What You Do Support the development and maintenance of data pipelines, ingestion processes, and data transformations. Create and maintain SQL queries, Python scripts, and Spark-based workloads used for data processing and analytics. Assist in troubleshooting pipeline failures, data quality issues, and operational incidents. Work with senior engineers to implement schema mappings, transformation logic, and data validation rules. Ensure datasets meet expected schemas, data contracts, and quality standards. Support metadata management, dataset documentation, and lineage activities. Assist in maintaining data classification information according to company standards. Help automate repetitive operational and data management tasks to improve efficiency and reliability. Contribute to monitoring, alerting, and operational support for data pipelines and workflows. Participate in testing activities, including unit tests, transformation validation, and data quality checks. Follow established engineering standards, coding practices, and team development patterns. Learn and apply security, privacy, and compliance requirements when handling sensitive or regulated data. Collaborate with Data Governance, Security, and Compliance teams when required. Contribute to continuous improvement initiatives focused on data trust, reliability, and operational excellence. Requirements and Qualifications Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Software Engineering, or related fields. Basic to intermediate English. Up to 2 years of experience in Data Engineering, Software Engineering, Data Analytics, or related areas. Knowledge of SQL and Python. Understanding of ETL/ELT concepts and data transformation processes. Familiarity with relational databases and data warehousing concepts. Basic knowledge of Spark, Databricks, or distributed data processing frameworks. Familiarity with Git and version control workflows. Basic understanding of cloud