全职 Data Engineer 工作, 薪水, Keyrus 公司招聘中 - Ricebowl

Keyrus is an international consulting firm, specializing in the integration of data intelligence and Digital solutions. With over 4000 employees spread across 27 countries, Keyrus continues to deliver on such projects to a wide range of clients from various industries including but not limited to Banking/Finance, Healthcare/pharmaceuticals, FMCG, Oil & Gas, and more.

As part of Keyrus’ solution delivery, we are also in a position to recruit and place technical consultants to complement on existing client projects with their expertise. As such, we seek innovative and agile people to support ambitious and forthcoming technological challenges.

About the Role

We are looking for a Data Engineer Consultant to support enterprise data transformation initiatives. You will be responsible for developing and optimizing data pipelines, enabling scalable data solutions, and supporting analytics and reporting needs across the organization.

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines from multiple data sources.
• Build and optimize data solutions using Databricks and cloud technologies.
• Develop and maintain data models to support analytics and reporting.
• Ensure data quality, integrity, and performance across data platforms.
• Support CI/CD and deployment processes for data engineering workflows.
• Collaborate with business and technical stakeholders to gather requirements and deliver solutions.

Requirements

• 3–5 years of experience in Data Engineering or related fields.
• Strong proficiency in Python and SQL.
• Experience with Databricks and/or Azure Data Platform.
• Hands-on experience with ETL/ELT development and data modelling.
• Familiarity with cloud-based data solutions and data warehousing concepts.
• Strong problem-solving and stakeholder communication skills.

Nice to Have

• PySpark experience.
• Azure DevOps and CI/CD exposure.
• Knowledge of NoSQL databases.
• Experience with Power BI or other BI tools.
• Understanding of data governance and data quality practices.