Data Engineer (Cloud & Analytics Platform)

About the role

The incumbent designs, builds and maintains scalable data solutions that support analytics, machine learning and business insights. The role covers data pipelines, cloud-based data platforms, analytics solutions and AI/ML support, with a focus on data quality, governance and operational excellence.

Key responsibilities

• Design, develop and maintain ETL/ELT pipelines across cloud and on-premise environments, integrating internal and external data sources
• Build, optimize and maintain scalable data models (e.g. star and snowflake schemas) for analytics, reporting and business intelligence
• Ensure data quality, integrity and availability through validation, cleansing, transformation and monitoring controls
• Manage cloud data solutions on Microsoft Azure (Azure Data Factory, Data Lake Storage, Azure SQL) and support infrastructure configuration, compute, storage, networking, identity and security
• Implement CI/CD pipelines, automate deployments and set up monitoring, logging and alerting
• Support interactive dashboards and reports in Power BI, and collaborate with Data Scientists to deploy and monitor machine learning models in production
• Work with business stakeholders to turn requirements into data solutions, and follow data governance, security, compliance and software engineering best practices

Required profile

• Bachelor's degree in Computer Science, Computer Engineering or a related field
• 2-3 years of experience in data engineering, software engineering or related roles with exposure to data platforms, cloud technologies and analytics solutions
• Analytical thinking, problem-solving and troubleshooting skills, good communication and collaboration skills
• Self-motivated, detail-oriented, able to manage multiple priorities and willing to learn new technologies

Required skills

• Data warehousing, data modeling, database optimization and ETL/ELT with Python or tools such as SSIS or Informatica
• Relational databases (Microsoft SQL Server, Oracle) and big data tools (Spark, Databricks)
• Python, Azure services (Azure Data Factory, Data Lake Storage, Azure SQL), CI/CD and DevOps tools such as Azure DevOps
• Power BI (DAX, Power Query), basic machine learning workflows (Scikit-learn, Azure Machine Learning)