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 platforms such as AWS, Azure, or Google Cloud.
• Knowledge of automation concepts and scripting for operational efficiency.
• Basic understanding of data quality concepts and validation practices.
• Familiarity with data governance principles, including metadata, ownership, stewardship, and documentation.
• Basic knowledge of data classification concepts (Public, Internal, Confidential, Restricted).
• Understanding of data lineage and traceability concepts.
• Awareness of security best practices, including access management, secrets management, and least-privilege principles.
• Strong analytical, problem-solving, and communication skills.
• Willingness to learn new technologies and collaborate across teams.

Security, Compliance & Governance

• Follow company standards for handling sensitive and regulated data.
• Apply data classification requirements when creating or maintaining datasets and pipelines.
• Use approved authentication, authorization, and secrets management mechanisms.
• Avoid exposing sensitive information through logs, exports, testing data, or documentation.
• Support auditability by maintaining documentation, metadata, and lineage information.
• Escalate security, privacy, or compliance concerns when requirements are unclear.
• Follow established governance processes and contribute to improving data trust across the organization.

How You Work

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