Senior Analytics Engineer

Maya Bank; Manila; On-site; PHP 1,200,000 - 2,000,000; Full time

Job summary

Maya Bank is seeking a Senior Analytics Engineer to design, develop, and monitor data products and pipelines supporting data science workflows. You will build feature stores, implement data quality checks, and collaborate with governance to mature data infrastructure.

You will mentor junior engineers, drive CI/CD for data pipelines, and communicate complex architectures to diverse audiences while documenting systems and architecture.

Qualifications

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

Responsibilities

• Design datasets with Data Scientists for ML models
• Develop and maintain feature stores and pipelines for training and real-time features
• Implement data quality checks and ensure data source SLA adherence
• Collaborate with Data Engineering and Governance to mature data
• Create and maintain software packages for Data Scientists' workflows
• Build CI/CD pipelines to accelerate deployment of data pipelines
• Provide guidance on code and architecture and perform reviews for best practices
• Communicate architecture and solutions to broad audiences
• Document architecture and systems clearly
• Build infrastructure enabling self-serve pipelines and mentor others

Skills

Cloud data warehouses

SQL/Spark

ETL/ELT pipelines

dbt for ETL

Mentoring junior engineers

Python/R/Bash

Airflow

Agile/DevOps/TDD

Stakeholder communication

Tools

Airflow

Job description

CORE PROFILE

As a Senior Analytics Engineer, you will be responsible for the design, development and monitoring of data products, data infrastructure, packages and processes that will help streamline the creation and deployment of data science solutions made by our Data Scientists.

NATURE OF WORK

• Work with our Data Scientists to design datasets that are useful for creating statistical and machine-learning models
• Design, develop and maintain feature stores as well as the accompanying feature pipelines that will be used in creating training data as well as real-time inference features.
• Implement data quality and integrity checks and ensure the quality and availability of data sources in accordance with their SLAs.
• Align with Data Engineering and Data Governance team to achieve maturity in the data.
• Create and maintain software packages for use by our Data Scientists to help improve their model development workflow.
• Build CI/CD pipelines to improve time to deploy data pipelines and proactively catch issues before they hit production
• Provide guidance on best practices for code and architecture of data pipelines and do code and architecture reviews to ensure adherence to best practices
• Communicate technical architecture and solutions, as well as explain the competitive advantage of various technologies to a broad audience
• Create and maintain architecture and systems documentation
• Build the infrastructure and tooling that enable self-serve pipelines for other Analytics Engineers, and mentor them on best practices

REQUIRED QUALIFICATIONS

• With at least a bachelor's degree in any quantitative discipline (i.e. Computer Science, Math, Physics, etc)
• At least 4 years of experience building and maintaining production ETL/ELT pipelines, including at least 1 year in a technical leadership or mentoring capacity (e.g., leading design reviews, setting team standards, onboarding junior engineers)
• At least 4 years of experience managing stakeholders across technical and non-technical audiences
• Demonstrated experience using dbt for data transformation in a production environment

DISPLAYED SKILL MASTERY

• High proficiency in cloud data warehouses (Redshift, Databricks, etc.) and SQL/Spark for data manipulation
• High proficiency in designing, developing, and monitoring ETL/ELT pipelines
• At least 5 years working with AWS or another cloud provider (GCP, Azure)
• Moderate experience (at least 3 years) with common data science tools, packages (Pandas, SKLearn), and concepts
• Proven experience using dbt for ETL
• Strong programming skills (Python, R, or Bash for pipeline development)
• Experience designing data models (dimensional modeling, star/snowflake schema)
• Experience with orchestration tools (Airflow, Dagster, or similar)
• Experience mentoring junior engineers and setting technical standards/best practices
• Experience working in Agile/DevOps/TDD environments
• Strong stakeholder communication skills across technical levels