About Us
Mercedes-Benz Tech Malaysia (MBTMY), established in 2003, is a global technology hub within the Mercedes-Benz Group. We play a key role in the company’s digital transformation by delivering innovative digital solutions across the entire value chain.
With over 300 technology professionals, MBTMY contributes to a wide range of areas including software engineering, data and analytics, cybersecurity, cloud infrastructure, DevOps and artificial intelligence. From enhancing in-car digital experiences to improving global vehicle sales platforms and advancing AI and security initiatives, our teams help drive high-impact technology solutions for Mercedes-Benz worldwide. Our culture is built on agility, collaboration and continuous learning.
We provide an environment where innovation can flourish, supported by strong technical leadership, cross-functional teamwork and a shared commitment to quality and excellence. We also offer flexible hybrid work arrangements, allowing team members to balance remote work with in-person collaboration at our vibrant Wisma Mercedes-Benz office in Puchong. A space designed to inspire creativity, innovation and meaningful connection.
Job Description
- Design, develop, and maintain scalable Data Engineering solutions on Microsoft Azure.
- Build and optimize data pipelines using Azure Databricks, Azure Data Factory and Azure Data Lake Storage.
- Develop robust ETL/ELT pipelines to process structured, semi-structured and unstructured datasets.
- Implement data models, data quality frameworks and governance controls to support enterprise analytics and reporting.
- Collaborate with business stakeholders, analysts and product teams to understand requirements and translate them into scalable technical solutions.
- Develop and maintain Power BI datasets, reporting layers and analytical data products.
- Build and support CI/CD pipelines using Azure DevOps and GitHub.
- Monitor, troubleshoot and optimize data platform performance, reliability and scalability.
- Implement security best practices including Azure Key Vault integration, role-based access controls and data governance standards.
- Contribute to system architecture design, documentation and technical knowledge sharing.
- You build it, you test it, you run it.
Qualifications
- Minimum 5 years of working experience in Data Engineering, Data Platform Engineering or related fields.
- For senior positions, minimum 7 to 10 years of relevant experience preferred.
- Degree in Computer Science, Information Technology, Data Engineering, Data Science or related discipline.
Experience
- Experience working in Agile/Scrum environments.
- Experience with Azure Data Platform technologies.
- Experience developing enterprise-grade ETL/ELT pipelines.
- Experience supporting data warehouse, data lake and analytics platforms.
- Experience working with source control tools such as GitHub or Azure Repos.
- Experience with Jira and Confluence.
Specific Knowledge / Skills
- Strong analytical and problem-solving skills.
- Passionate about improving data quality, platform reliability and scalability.
- Strong stakeholder management and communication skills.
- Ability to work independently while collaborating across multiple teams.
- Product-oriented and results-driven mindset.
Knowledge and Skills
Data Engineering
- Strong SQL and Python/PySpark development.
- Experience building and supporting ETL/ELT pipelines.
- Data modelling experience including Star Schema, Snowflake Schema and Medallion Architecture.
- Experience with batch and streaming data processing.
- Experience developing data quality, validation and monitoring frameworks.
Cloud & Platform
- Microsoft Azure
- Azure Databricks
- Azure Data Factory
- Azure Data Lake Storage (ADLS)
- Azure Synapse Analytics (nice to have)
- Azure Key Vault
- Azure DevOps Pipelines
- GitHub Actions
Databricks & Big Data
- Apache Spark / PySpark
- Delta Lake
- Databricks Workflows
- Unity Catalog (nice to have)
- Hive Metastore (nice to have)
Visualization & Reporting
- Power BI
- DAX
- Data Analytics and Reporting
DevOps & Engineering
- GitHub / Azure Repos
- CI/CD
- Docker
- Terraform (nice to have)
- Databricks CLI / Azure CLI (nice to have)
Nice to Have
- FastAPI
- Redis
- NATS
- Kusto Query Language (KQL)
- Azure Log Analytics
- PowerShell / Bash
Disclaimer
Please note that this position may be offered under a third-party employment arrangement, subject to the final hiring and engagement model. Regardless of the employment arrangement, the selected candidate will be assigned to support and work closely with Mercedes-Benz Malaysia, following Mercedes-Benz's project requirements and day-to-day operations.