Data Engineer

iGaming Centre; Manila; On-site; PHP 800,000 - 1,400,000; Full time

Job summary

iGaming Centre in Manila is seeking a Data Engineer who turns data into insights and builds the infrastructure powering decision-making. You’ll collect, store, process, and analyze data, selecting the best tools and ensuring they integrate into our architecture.

The role emphasizes data quality, scalable pipelines with Spark, dashboards for stakeholders, and ongoing improvements. Strong OO design, Unix/Linux, Java/Scala, and RESTful APIs are valued.

Qualifications

• Solid foundation in object-oriented design and development.
• Unix/Linux systems and shell scripting.
• Java/Scala programming.
• Building and consuming RESTful APIs.
• Working with SQL and NoSQL databases; data modeling.
• Experience with Hadoop, HBase, Hive, Kafka, Storm is a plus.
• Streaming architectures familiarity is a plus.
• Strong English communication, written and spoken.

Responsibilities

• Monitor and continuously improve data quality and reliability.
• Develop insightful reports and dashboards for stakeholders.
• Build and maintain scalable data pipelines using Spark.
• Collaborate with teams to optimize data infrastructure.

Skills

Unix/Linux

Shell scripting

Java/Scala

RESTful APIs

SQL

NoSQL

Data modeling

English communication

Big data basics

Streaming architectures

Tools

Spark

Hadoop

HBase

Hive

Kafka

Storm

Job description

We're on the lookout for a Data Engineer who’s passionate about turning data into insights and building the infrastructure that powers decision‑making. You'll be at the heart of collecting, storing, processing, and analyzing data sets selecting the best tools for the job, implementing and maintaining them, and ensuring they integrate seamlessly into our overall architecture.
We're on the lookout for a Data Engineer who’s passionate about turning data into insights and building the infrastructure that powers decision‑making. You'll be at the heart of collecting, storing, processing, and analyzing data sets selecting the best tools for the job, implementing and maintaining them, and ensuring they integrate seamlessly into our overall architecture.

Requirements

• Solid foundation in object-oriented design and development
• Practical experience with:
• Unix/Linux systems and shell scripting
• Java/Scala programming
• Building and consuming RESTful APIs
• Working with SQL and NoSQL databases, including understanding data modeling and trade-offs
• Managing columnar, distributed, and row-based databases
• Strong research skills to discover and apply best practices
• Passion for improving systems, optimizing processes, and exploring new tech
• Proactive, self-motivated, and results-focused
• Excellent English communication skills, both written and spoken
• Experience with open-source big data technologies (Hadoop, HBase, Hive, Kafka, Storm, etc.) is a plus but not required.
• Familiarity with streaming architectures is a plus but not required
• Solid foundation in object-oriented design and development
• Practical experience with:
• Unix/Linux systems and shell scripting
• Java/Scala programming
• Building and consuming RESTful APIs
• Working with SQL and NoSQL databases, including understanding data modeling and trade-offs
• Managing columnar, distributed, and row-based databases
• Strong research skills to discover and apply best practices
• Passion for improving systems, optimizing processes, and exploring new tech
• Proactive, self-motivated, and results-focused
• Excellent English communication skills, both written and spoken
• Experience with open-source big data technologies (Hadoop, HBase, Hive, Kafka, Storm, etc.) is a plus but not required.
• Familiarity with streaming architectures is a plus but not required

Responsibilities

• Monitor and continuously improve data quality and reliability
• Recommend new approaches to make our data even more robust and actionable
• Develop insightful reports and dashboards for various stakeholders
• Build and maintain scalable data pipelines using Spark
• Monitor and continuously improve data quality and reliability
• Recommend new approaches to make our data even more robust and actionable
• Develop insightful reports and dashboards for various stakeholders
• Build and maintain scalable data pipelines using Spark