Data Engineer – Build Scalable Cloud Data Pipelines

About the role

We are looking for a skilled Data Engineer to design, build, and optimise scalable data solutions that power analytics, reporting, and business intelligence across modern cloud platforms. You will create reliable, analytics‑ready datasets and ensure data quality, governance, and security.

Key responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines and workflows.
• Build data ingestion solutions from ERP systems, APIs, databases, and external sources.
• Develop and support data warehouse and lakehouse architectures for efficient reporting.
• Implement batch and real‑time processing using tools such as Apache Spark (PySpark).
• Monitor, troubleshoot, and optimise pipelines for performance, scalability, reliability, and cost.
• Ensure data quality, lineage, governance, and compliance.
• Automate workflows and implement CI/CD practices.
• Collaborate with analysts, BI teams, and IT stakeholders to deliver data requirements.
• Maintain technical documentation for pipelines, integrations, and data models.

Required profile

• Bachelor’s degree in Computer Science, IT, Data Science or related field.
• 3–7 years of experience in data engineering or a closely related role.
• Strong proficiency in SQL and Python for data processing.
• Hands‑on experience with Apache Spark (PySpark preferred).
• Experience with cloud platforms such as Azure, AWS, Google Cloud, Databricks or Microsoft Fabric.
• Solid understanding of data warehousing, lakehouse concepts and data governance.
• Experience integrating data from ERP systems, APIs and relational databases.
• Familiarity with CI/CD pipelines and automated data workflows.

Required skills

• SQL
• Python
• Apache Spark / PySpark
• Azure
• AWS
• Google Cloud Platform
• Databricks
• Microsoft Fabric
• ETL/ELT design
• CI/CD automation
• ERP system integration
• API integration
• Relational databases