Azure Databricks Data Engineer

Role Description The Azure Data Engineer role is a full-time, 18‑month contract position based on-site in Singapore. The role involves designing, building, and maintaining scalable data pipelines on Microsoft Azure, including ingestion, transformation, and integration of data from multiple sources. The Azure Data Engineer will develop and optimize data models, support data warehousing solutions, and ensure data quality, reliability, and security across environments. Day-to-day responsibilities include collaborating with business and analytics teams to understand data requirements, implementing ETL processes, monitoring performance, and troubleshooting issues in production workflows. The role also includes preparing technical documentation, contributing to best practices, and supporting continuous improvement of data engineering standards.

Qualifications

Strong data engineering skills, including experience with Azure data services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks) and pipeline orchestration.

Proficiency in data modeling and data warehousing concepts, including designing schemas and optimizing storage for analytics workloads.

Hands-on experience with Extract Transform Load (ETL) processes, including building, maintaining, and optimizing ETL/ELT workflows.

Capability in data analytics, with the ability to collaborate with data analysts and data scientists to enable reporting, dashboards, and advanced analytics.

Strong skills in SQL and at least one programming language commonly used in data engineering (such as Python or Scala).

Experience with cloud security, data governance, and best practices for data quality, monitoring, and incident resolution.

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.

Effective communication and collaboration skills, with the ability to work in cross-functional teams and an on-site environment.

Experience in large-scale or enterprise data environments and prior work with BI tools (e.g., Power BI) is an advantage.