Data Engineer (Cloud & Analytics Platform)

About the role

We are looking for a Data Engineer to design, build and maintain scalable data solutions on Microsoft Azure. The role supports analytics, machine‑learning and business‑insight initiatives by delivering robust pipelines, data models and reporting layers.

Key responsibilities

• Design, develop and maintain ETL/ELT pipelines across cloud and on‑premise environments, integrating internal and external data sources.
• Build and optimise scalable data models (star and snowflake schemas) for analytics and BI reporting.
• Ensure data quality, integrity and availability through validation, cleansing and monitoring controls.
• Implement and manage Azure‑based data services such as Azure Data Factory, Data Lake Storage and Azure SQL.
• Configure and administer compute, storage, networking, identity and security controls for reliable platform operations.
• Set up CI/CD pipelines, automate deployments and establish monitoring, logging and alerting.
• Support the creation of interactive Power BI dashboards and reports.
• Collaborate with business stakeholders and data scientists to translate requirements into effective data solutions and AI/ML integrations.
• Maintain data governance, security, compliance and lineage while following 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 or software engineering.
• Strong analytical, problem‑solving and troubleshooting abilities.
• Effective communication and collaboration skills across technical and business teams.
• Self‑motivated, detail‑oriented and able to manage multiple priorities in a dynamic environment.
• Willingness to learn new technologies, tools and methodologies.

Required skills

• Microsoft Azure services (Azure Data Factory, Data Lake Storage, Azure SQL).
• ETL/ELT pipeline development.
• Data warehousing concepts and data modeling (star and snowflake schemas).
• Database optimisation techniques.
• Power BI for dashboard and report development.
• CI/CD implementation and automation.
• Version control (e.g., Git) and code review practices.
• Monitoring, logging and alerting setup.
• Data governance, security, compliance and data lineage.