全职 Senior Data Backend Engineer (Python - Databricks - Azure) 工作, 薪水, Luxoft Malaysia WP Kuala Lumpur 公司招聘中 - Ricebowl

Project Description

Join the development of a Sales Planning and Analytics Platform for a premium German automotive OEM operating across the Asia region. This platform enables data-driven business decisions through scalable data processing, enterprise data integration, and API-based data consumption.

As part of a collaborative engineering team, you will design and develop robust data solutions that support business planning, analytics, and cloud-native applications on Microsoft Azure.

Responsibilities

• Design and develop scalable data processing solutions using Python and Databricks.
• Build and maintain data pipelines for data ingestion, transformation, and aggregation.
• Develop RESTful APIs using FastAPI to expose data and analytical services to consuming applications.
• Design and optimize SQL queries and data models.
• Collaborate with Business Analysts, Data Engineers, and Product Stakeholders to translate business requirements into technical solutions.
• Deploy, maintain, and optimize solutions on Microsoft Azure.
• Drive engineering best practices in performance optimization, monitoring, testing, and code quality.
• Mentor junior engineers and contribute to technical architecture and design decisions.

Mandatory Skills

• 5+ years of software engineering or data engineering experience.
• Strong development experience with Python.
• Hands-on experience with Databricks.
• Strong SQL skills and experience with data modeling.
• Experience developing REST APIs using FastAPI.
• Experience working with Microsoft Azure cloud services.
• Good understanding of ETL/ELT concepts and data pipeline architecture.
• Experience with Git and CI/CD pipelines.

Nice-to-Have Skills

• Experience with Azure Data Factory (ADF).
• Experience with Azure Synapse Analytics.
• Knowledge of Apache Spark and PySpark.
• Experience building data products for Sales, Marketing, or Business Planning.
• Exposure to Machine Learning workflows and data science pipelines.