Data Engineer

EiVA HR Solutions; Metro Manila; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

EiVA HR Solutions in Metro Manila is seeking an experienced Data Engineer to design, develop, and maintain an open-source data platform focused on batch processing. You will build scalable data infrastructure including a data lakehouse using Amazon S3, Iceberg, and ClickHouse, and develop ETL/ELT pipelines with Spark.

The role requires strong SQL, data modeling, and Python/Scala/Java programming, plus hands-on experience with distributed data systems and AWS.

Qualifications

• Minimum of 3 years of experience as a Data Engineer or in a similar data infrastructure role.
• Strong proficiency in SQL and data modeling.
• Experience with data lake/lakehouse architectures (Iceberg, S3).
• Experience with analytical or columnar databases (ClickHouse).
• Experience building and orchestrating ETL/ELT pipelines.
• Strong programming skills in Python and/or Scala/Java.
• Experience with microservices architecture and AWS.
• Hands-on with Apache Spark for large-scale data processing.
• Proficiency in English (written and spoken).
• Excellent communication and multitasking skills.

Responsibilities

• Develop a scalable data platform integrating multiple data sources for easy access.
• Design and enhance data tools including orchestration, governance, data lakehouse, and BI solutions.
• Ensure smooth operation of data systems for analysts, data scientists, and engineers.
• Optimize data pipelines for ingestion, processing, and output within a microservices environment.
• Build, maintain, and monitor ETL/ELT processes.
• Troubleshoot and improve performance, scalability, and reliability of data infrastructure (S3, Iceberg, ClickHouse).
• Collaborate with data scientists, analysts, and backend engineers to understand data needs.
• Implement and promote data quality, governance, and security best practices across the platform.
• Develop and maintain batch data processing workflows using Spark or similar technologies.

Skills

SQL

Data modeling

Python/Scala/Java

ETL/ELT pipelines

Apache Spark

Cloud environments (AWS)

Data Lakehouse (Iceberg, S3, ClickHose

Tools

Apache Iceberg

Amazon S3

ClickHouse

Apache Spark

Python

Scala/Java

Docker

Kubernetes

Job description

We are looking for an experienced and self-motivated Data Engineer to design, develop, manage, and maintain an open-source data platform. The role focuses on building and optimizing scalable data infrastructure, including a Data Lakehouse using Amazon S3, Apache Iceberg, and ClickHouse.

The ideal candidate has strong SQL and programming skills, hands-on experience with ETL/ELT pipelines and Apache Spark, and the ability to collaborate effectively with data scientists, analysts, and backend engineers.

Focus on data platform development, ETL/ELT pipelines, batch processing, and data infrastructure optimization. This role focuses on batch data processing, not real-time streaming.

Responsibilities

• Develop a scalable data platform integrating multiple data sources for easy access.
• Design and enhance data tools, including orchestration, governance, data lakehouse, and business intelligence solutions.
• Ensure the smooth operation of data systems for analysts, data scientists, and engineers.
• Optimize data pipelines for ingestion, processing, and output within a microservices environment.
• Build, maintain, and monitor ETL/ELT processes.
• Troubleshoot and improve the performance, scalability, and reliability of data infrastructure, including Amazon S3, Apache Iceberg, and ClickHouse.
• Collaborate with data scientists, analysts, and backend engineers to understand data needs and deliver effective solutions.
• Implement and promote data quality, governance, and security best practices across the platform.
• Develop and maintain batch data processing workflows using Apache Spark or similar technologies.

Qualifications

• Minimum of 3 years of experience as a Data Engineer or in a similar data infrastructure role.
• Strong proficiency in SQL and hands-on experience with data modeling.
• Experience with data lake and lakehouse architectures, such as Apache Iceberg, Amazon S3, or similar technologies.
• Experience with analytical or columnar databases, such as ClickHouse or equivalent platforms.
• Experience building and orchestrating ETL/ELT pipelines.
• Strong programming skills in Python and/or Scala/Java.
• Experience working with microservices architecture and cloud environments, preferably AWS.
• Hands-on experience with Apache Spark or similar technologies for large-scale data processing.
• Professional proficiency in written and spoken English.
• Strong communication and multitasking skills, with the ability to work independently and collaboratively.
• Self-motivated, detail-oriented, and committed to maintaining reliable data infrastructure

Required Skills

• Strong proficiency in SQL.
• Hands-on experience with data modeling.
• Experience with data lake and lakehouse architectures, such as Apache Iceberg, Amazon S3, or similar technologies.
• Experience with analytical or columnar databases, such as ClickHouse or equivalent platforms.
• Experience building and orchestrating ETL/ELT pipelines.Strong programming skills in Python and/or Scala/Java.
• Experience working with microservices architecture and cloud environments, preferably AWS.
• Hands-on experience with Apache Spark or similar technologies for large-scale data processing.
• Professional proficiency in written and spoken English.
• Strong communication and multitasking skills, with the ability to work independently and collaboratively.
• Self-motivated, detail-oriented, and committed to maintaining reliable data infrastructure
• Focus on data platform development, ETL/ELT pipelines, batch processing, and data infrastructure optimization
• This role focuses on batch data processing, not real-time streaming.

Preferred Skills

• Experience working with or contributing to open-source data platforms and tools.
• Familiarity with business intelligence and visualization tools, such as Apache Superset, Looker, Tableau, or Metabase.
• Experience with containerization and orchestration technologies, including Docker and Kubernetes.
• Familiarity with infrastructure-as-code and CI/CD practices.
• Experience with AWS EMR and running Apache Spark workloads in cloud environments.
• Experience using AI-assisted development tools, such as GitHub Copilot, Cursor, or similar platforms, to improve engineering productivity.