Data Engineer – Build Scalable ETL Pipelines

About the role

We are looking for a Data Engineer to design, develop and maintain scalable ETL/ELT pipelines that feed our enterprise data platforms. The role focuses on extracting, transforming and loading data from multiple source systems to support reporting, analytics and machine‑learning initiatives.

Key responsibilities

• Design, develop and maintain ETL/ELT pipelines using Python, PySpark and SQL.
• Extract, transform and load data from diverse source systems into enterprise data platforms.
• Create reusable transformation logic for business reporting, analytics and ML projects.
• Optimize SQL queries and PySpark jobs for performance and efficiency.
• Build and maintain data models, staging layers and curated datasets for downstream consumption.
• Perform data cleansing, validation, reconciliation and quality checks.
• Monitor scheduled ETL jobs and proactively address operational issues.

Required profile

• Ability to troubleshoot and resolve pipeline failures, performance bottlenecks and data‑related issues.
• Experience collaborating with business analysts, data architects and data scientists to understand requirements.
• Participation in code reviews, testing, deployment and production support activities.
• Capability to develop and maintain technical documentation, including data mappings and transformation logic.
• Commitment to data‑governance, security and regulatory compliance.

Required skills

• Python
• PySpark
• SQL