Azure Data Platform Engineer (Databricks & ETL)

AIA; Philippines; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

MediCard Phils., Inc. seeks a Data Engineer to design and operate scalable data platforms on Microsoft Azure. You will build end-to-end ETL/ELT pipelines, handle data modeling and storage in a data lake, and ensure data quality and security across the enterprise.

Collaborate with data analysts, BI teams, and business stakeholders to translate requirements into robust data solutions and scalable architectures.

Qualifications

• Experience designing scalable Azure-based data platforms.
• Proficiency in building and maintaining ETL/ELT pipelines.
• Hands-on with Databricks, PySpark, and Delta Lake.
• Ability to model, store, and optimize analytics data.
• Strong focus on data quality, validation, and reliability.
• Knowledge of security, governance, and compliance.
• Experience with CI/CD for data pipelines and IaC concepts.
• Ability to translate business needs into technical data solutions.

Responsibilities

• Design, build, and maintain scalable data platforms on Microsoft Azure.
• Develop and orchestrate ETL/ELT pipelines using ADF and Databricks.
• Implement data transformation using Databricks (PySpark, Spark SQL).
• Model data for analytics and manage Azure Data Lake storage.
• Enforce data quality, monitoring, and data reconciliation processes.
• Apply security, RBAC, and governance across data pipelines.
• Automate deployments and monitoring for stability and cost efficiency.
• Collaborate with analysts, scientists, and business stakeholders.

Skills

Data platform design

ETL/ELT pipelines

Databricks engineering

Data modeling

Data quality & reliability

Cloud security & governance

DevOps & automation

Stakeholder collaboration

Tools

Azure Databricks

Azure Data Factory

Azure Data Lake

Delta Lake

PySpark

Spark SQL

Job description

MediCard Phils., Inc. seeks a Data Engineer to design and operate scalable data platforms on Microsoft Azure. You will build end-to-end ETL/ELT pipelines, handle data modeling and storage in a data lake, and ensure data quality and security across the enterprise.

Collaborate with data analysts, BI teams, and business stakeholders to translate requirements into robust data solutions and scalable architectures.