Microsoft Azure Data Services  

Responsible for designing, developing, and optimizing big data solutions using the Databricks platform. This role involves close collaboration with data engineers, data scientists, and stakeholders to ensure efficient data processing, quality, and performance
Key Responsibilities
• Develop and implement advanced data solutions on Databricks using Python, Scala, or SQL.
• Optimize ETL processes and data pipelines for performance and reliability.
• Integrate Databricks notebooks with tools like Azure Data Factory and Synapse Analytics
• Implement secret scopes and secure data access using Databricks CLI and Azure Service Principals.
• Collaborate with cross-functional teams to understand data requirements and deliver scalable solutions.
• Conduct performance tuning and troubleshoot data workflows.
• Document processes and maintain high standards of code quality and maintainability.
Qualifications
• 5+ years of experience in data engineering or similar roles.
• Strong proficiency in Databricks, Apache Spark, and cloud platforms (Azure).
• Experience with data modeling, data cleansing, and warehousing at scale (1TB+).
• Familiarity with Agile delivery paradigms and software engineering best practices.
• Knowledge of core Python libraries (NumPy, Pandas, Matplotlib) is a plus
Skills
• Databricks, Apache Spark
• Python, SQL, Scala
• Azure Data Factory, Synapse Analytics
• ETL, Data Modeling, Performance Tuning
• Cloud platforms: Azure
• CI/CD and Git integration workflows

Must have and Good to have skills: Databricks, Apache Spark, Azure Data Factory, Synapse Analytics

Number of years experiences needed: 4-5 years

Shift Schedule: EMEA

Work Set up: Hybrid

Number of days in a month or week that needs to be in the office: 2 x A week (can be discussed)

Project Location (During onsite): Uptown Tower 3, BGC Taguig City

• Strong analytical and planning skills;
• Good communication and presentation skills;
• Excellent problem-solving skills;
• Develop and implement advanced data solutions on Databricks using Python, Scala, or SQL.
• Optimize ETL processes and data pipelines for performance and reliability.
• Integrate Databricks notebooks with tools like Azure Data Factory and Synapse Analytics
• Implement secret scopes and secure data access using Databricks CLI and Azure Service Principals