Lead Analytics Engineer: Data Pipelines & ML Tools

Maya; Manila; On-site; PHP 1,500,000 - 2,100,000; Full time

Job summary

Maya is seeking a Senior Analytics Engineer to design, develop, and monitor data products and infrastructure that streamline data science workflows. You will contribute to feature stores and pipelines for training data and real-time features.

Collaborate with Data Scientists, Data Engineers, and Governance teams to ensure data quality, scalability, and reliability across the platform and empower data-driven decisions.

Qualifications

• Bachelor's degree in a quantitative field such as CS, Math, Physics.
• 4+ years building/maintaining production ETL/ELT pipelines; 1 year in leadership/mentoring.
• 4+ years managing stakeholders across technical and non-technical audiences.
• Demonstrated experience using dbt for data transformation in production.

Responsibilities

• Design datasets with Data Scientists for statistics and ML models.
• Develop and maintain feature stores and feature pipelines for training and real-time inference.
• Implement data quality/integrity checks and ensure data source SLAs.
• Align with Data Engineering and Data Governance to mature data practices.
• Create and maintain software packages for Data Scientists to improve model development workflows.
• Build CI/CD pipelines to deploy data pipelines and catch issues pre-production.
• Provide guidance on code/architecture best practices and conduct reviews.
• Communicate architecture/solutions to broad audiences.
• Document architecture and systems.
• Build tooling to enable self-serve pipelines for Analytics Engineers and mentor them.

Skills

Cloud data warehouses

SQL/Spark

ETL/ELT pipelines

AWS/GCP/Azure

Data modeling

Python/R

dbt

Data governance

Stakeholder management

Mentoring

Education

Bachelor's degree in a quantitative discipline

Tools

dbt

Redshift

Databricks

Airflow

Job description

Maya is seeking a Senior Analytics Engineer to design, develop, and monitor data products and infrastructure that streamline data science workflows. You will contribute to feature stores and pipelines for training data and real-time features.

Collaborate with Data Scientists, Data Engineers, and Governance teams to ensure data quality, scalability, and reliability across the platform and empower data-driven decisions.