Senior Analytics Engineer: Build Scalable ML Data Platforms

Maya; Metro Manila; On-site; PHP 900,000 - 1,400,000; Full time

Job summary

Maya in Manila seeks an Analytics Engineer to design, develop, and maintain scalable data products, feature stores, data models, and transformation frameworks that enable Data Scientists to build, deploy, and operationalize machine learning solutions.

You will bridge Data Engineering and Data Science with strong experience in dbt, SQL, data modeling, Databricks, cloud platforms, and feature engineering pipelines to ensure trusted, analytics-ready data for ML use cases.

Qualifications

• Bachelor's degree in a quantitative field.
• 4+ years of experience in Analytics Engineering, Data Engineering, or related fields.
• 2+ years hands-on experience with dbt in production.
• Strong SQL, data modeling and ETL/ELT development.
• Experience with Databricks, Spark, or Lakehouse platforms.
• Experience with cloud technologies such as AWS, Azure, or GCP.
• Experience building CI/CD pipelines and automated testing frameworks.
• Cross-functional collaboration with Data Science, Data Engineering, and stakeholders.

Responsibilities

• Design, develop, and maintain scalable data models and transformation pipelines.
• Build and maintain feature stores and feature pipelines for model training and serving.
• Implement data quality checks, governance, and lineage across analytics assets.
• Create reusable software packages and tools; maintain architecture and documentation.
• Collaborate with Data Scientists to enable efficient ML experimentation and deployment.

Skills

SQL

Data modeling

Feature engineering

CI/CD pipelines

Databricks

Education

Bachelor's degree in Computer Science / Information Technology / Statistics / Engineering

Tools

dbt

Databricks

AWS / Azure / GCP

Job description

Maya in Manila seeks an Analytics Engineer to design, develop, and maintain scalable data products, feature stores, data models, and transformation frameworks that enable Data Scientists to build, deploy, and operationalize machine learning solutions.

You will bridge Data Engineering and Data Science with strong experience in dbt, SQL, data modeling, Databricks, cloud platforms, and feature engineering pipelines to ensure trusted, analytics-ready data for ML use cases.