Hybrid Data Engineer - Spark, ELT/ETL & Lakehouse

Emapta Global; Metro Manila; Hybrid; PHP 1,674,000 - 2,567,000; Full time

Benefits offered by this job

HMO coverage for dependents

Salary advance program

Office gyms access

Employee referral incentives

Job summary

Emapta is hiring a Data Engineer to design, build, and maintain batch and incremental ETL/ELT pipelines that turn raw data into analytics-ready datasets. You will work on Spark-based transformations (PySpark, Spark SQL) and apply data modeling to support BI use cases.

Collaborating with Analytics, Backend, and Product teams, you will maintain data warehouse and lakehouse environments, ensure data quality, and contribute to scalable, production-ready data solutions across global teams.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Technology, or related field.
• 3-7+ years of experience in data engineering or analytics engineering.
• Hands-on experience building and maintaining ETL/ELT pipelines.
• Experience with Apache Spark for large-scale data processing.
• Background in data integration, migration, and pipeline development.

Responsibilities

• Design, develop, and maintain batch and incremental ETL/ELT pipelines.
• Transform raw data into analytics-ready datasets.
• Build and optimize Spark-based data transformations using PySpark and Spark SQL.
• Apply data modeling best practices to support reporting and BI use cases.
• Maintain data warehouse and lakehouse environments.
• Implement data quality checks and monitoring for pipeline reliability.

Skills

Python

SQL

Apache Spark

Data modeling

ETL/ELT design

REST APIs

Git

Cloud knowledge (AWS/Azure/GC)

Education

Bachelor's degree in Computer Science / Data Engineering / IT

Tools

Databricks

Delta Lake

Spark/PySpark

Spark SQL

Data Warehouses / Lakehouse

Node.js / FastAPI

Job description

Emapta is hiring a Data Engineer to design, build, and maintain batch and incremental ETL/ELT pipelines that turn raw data into analytics-ready datasets. You will work on Spark-based transformations (PySpark, Spark SQL) and apply data modeling to support BI use cases.

Collaborating with Analytics, Backend, and Product teams, you will maintain data warehouse and lakehouse environments, ensure data quality, and contribute to scalable, production-ready data solutions across global teams.