Senior Analytics Engineer — Data Pipelines & Feature Stores

Maya; Manila; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

Maya is hiring a Senior Analytics Engineer to lead the design, development, and monitoring of data products and data infrastructure. You will collaborate with Data Scientists to create datasets, feature stores, and pipelines for training data and real-time inference features.

The role requires 4+ years building production ETL/ELT pipelines, 1+ year in leadership or mentoring, and strong skills in dbt, SQL, Python, Airflow, and cloud data warehouses.

Qualifications

• Bachelor's degree in a quantitative discipline (e.g., CS, Math, Physics)
• At least 4 years building and maintaining production ETL/ELT pipelines
• At least 1 year in technical leadership or mentoring
• At least 4 years managing stakeholders across technical and non-technical audiences
• Experience using dbt for data transformation in production environments

Responsibilities

• Design, develop and monitor data products and data infrastructure
• Build and maintain feature stores and pipelines for training data and real-time features
• Implement data quality and SLA-aligned data sources
• Collaborate with Data Engineering and Data Governance for data maturity
• Create and maintain software packages for Data Scientists to improve model workflows
• Develop CI/CD pipelines for data pipelines and proactive issue detection
• Provide architecture reviews and adherence to best practices
• Communicate architectural decisions to stakeholders and document systems

Skills

Stakeholder management

Leadership experience

Data product design

Data governance awareness

Education

Bachelor's degree in quantitative field

Tools

dbt

SQL

Spark

Python

Airflow

AWS

Redshift

Job description

Maya is hiring a Senior Analytics Engineer to lead the design, development, and monitoring of data products and data infrastructure. You will collaborate with Data Scientists to create datasets, feature stores, and pipelines for training data and real-time inference features.

The role requires 4+ years building production ETL/ELT pipelines, 1+ year in leadership or mentoring, and strong skills in dbt, SQL, Python, Airflow, and cloud data warehouses.