Data Engineer (Hybrid Cloud, Reporting, and Visualization) at Octal Philippines Inc.

📍 Job Overview

Job Title: Data Engineer (Hybrid Cloud, Reporting, and Visualization)
Company: Octal Philippines Inc.
Location: Quezon City, Metro Manila, Philippines
Job Type: Hybrid (On-site and Remote)
Category: Data Engineering
Date Posted: June 25, 2025
Experience Level: Mid-Senior level (2-5 years of experience)

🚀 Role Summary

Design, develop, and maintain scalable data pipelines and infrastructure across hybrid cloud environments
Build robust reporting and visualization solutions to provide actionable insights to stakeholders
Collaborate with data analysts, scientists, and business users to understand their requirements and deliver relevant data solutions
Ensure data quality, governance, and compliance through implementation of data validation, cleansing, and standardization processes
Troubleshoot complex data-related issues and find innovative solutions

📝 Enhancement Note: This role requires a strong focus on data engineering concepts, tools, and technologies, as well as experience with major cloud platforms and their data-related services. Familiarity with data analytics and machine learning concepts is a plus.

💻 Primary Responsibilities

Data Pipeline Development: Design, build, and maintain scalable data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses or data lakes
Hybrid Cloud Infrastructure: Manage and optimize data infrastructure across hybrid cloud environments, leveraging cloud-native services and on-premises resources
Data Quality: Ensure data quality through implementation of data validation, cleansing, and standardization processes
Reporting and Visualization: Develop interactive reports and dashboards using tools like Power BI, Tableau, or Looker to provide actionable insights to stakeholders
Data Governance: Adhere to data governance policies and procedures, including data security, privacy, and compliance regulations
Data Modeling: Design and implement data models (e.g., dimensional, normalized) to optimize data storage and retrieval
Automation: Automate data pipelines and processes using scripting languages (e.g., Python, SQL) and automation tools
Collaboration: Work closely with data analysts, scientists, and business users to understand their requirements and deliver relevant data solutions

📝 Enhancement Note: This role requires a strong technical background in data engineering, with a focus on data pipelines, cloud infrastructure, and reporting. Proficiency in data modeling techniques and data warehouse design is essential for success in this role.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Information Technology, or a related field. Relevant coursework or certifications in data engineering are a plus.

Experience: Proven experience as a Data Engineer or similar role with a focus on data pipelines, cloud infrastructure, and reporting.

Required Skills:

Strong understanding of data engineering concepts, tools, and technologies (e.g., SQL, Python, ETL tools, cloud platforms)
Experience with major cloud platforms (e.g., AWS, Azure, GCP) and their data-related services (e.g., data warehouses such as BigQuery/Redshift, data lakes, data pipelines)
Proficiency in data modeling techniques (e.g., dimensional, normalized) and data warehouse design
Expertise in using reporting and visualization tools (e.g., Looker, Power BI, Tableau) to create interactive dashboards
Excellent problem-solving skills to troubleshoot complex data-related issues and find innovative solutions
Strong communication skills to collaborate effectively with cross-functional teams

Preferred Skills:

Experience with data warehousing and data lake technologies (e.g., Google BigQuery, Amazon Redshift, Snowflake, Databricks)
Knowledge of data analytics and machine learning concepts
Familiarity with data governance and compliance frameworks (e.g., HIPAA, GDPR, CCPA)

📝 Enhancement Note: This role requires a strong technical background in data engineering, with a focus on data pipelines, cloud infrastructure, and reporting. Preferred skills include experience with data warehousing and data lake technologies, as well as knowledge of data analytics and machine learning concepts.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate your ability to design, develop, and maintain data pipelines and infrastructure across hybrid cloud environments
Showcase your experience with reporting and visualization tools, such as Power BI, Tableau, or Looker
Highlight your problem-solving skills and ability to troubleshoot complex data-related issues

Technical Documentation:

Document your data modeling techniques and data warehouse design processes
Showcase your understanding of data governance policies and procedures, including data security, privacy, and compliance regulations
Demonstrate your ability to automate data pipelines and processes using scripting languages and automation tools

📝 Enhancement Note: This role requires a strong technical background in data engineering, with a focus on data pipelines, cloud infrastructure, and reporting. Portfolio requirements should highlight your ability to design, develop, and maintain data pipelines and infrastructure, as well as your experience with reporting and visualization tools.

💵 Compensation & Benefits

Salary Range: PHP 60,000 - 120,000 per month (Mid-Senior level, based on experience and qualifications)

Benefits:

Health Insurance
Standard Statutory Benefits
Allowance

Working Hours: Full-time (40 hours per week), with flexible hours for deployment windows, maintenance, and project deadlines

📝 Enhancement Note: The salary range for this role is based on market research for data engineering roles in the Philippines, with consideration for the mid-senior level experience required. Benefits include health insurance, standard statutory benefits, and allowance.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology and Software Development

Company Size: Medium-sized company (201-500 employees)

Founded: 2010

Team Structure:

Data Engineering team: Responsible for designing, developing, and maintaining data pipelines and infrastructure, as well as building robust reporting and visualization solutions
Data Analytics team: Focuses on data analysis, machine learning, and business intelligence to provide actionable insights to stakeholders
Business teams: Collaborates with data engineering and analytics teams to understand their data requirements and deliver relevant data solutions

Development Methodology:

Agile/Scrum methodologies for sprint planning and project management
Code review, testing, and quality assurance practices for data pipelines and infrastructure development
Deployment strategies, CI/CD pipelines, and server management for hybrid cloud environments

Company Website: Octal Philippines Inc.

📝 Enhancement Note: This role is part of a medium-sized technology and software development company, with a focus on data engineering, analytics, and business intelligence. The company uses Agile/Scrum methodologies for project management and has a strong commitment to data quality, governance, and compliance.

📈 Career & Growth Analysis

Data Engineering Career Level: Mid-Senior level (2-5 years of experience) with a focus on data pipelines, cloud infrastructure, and reporting. This role offers opportunities for growth in technical leadership and architecture decision-making.

Reporting Structure: This role reports directly to the Data Engineering Manager, who is responsible for overseeing the data engineering team and ensuring the delivery of high-quality data solutions.

Technical Impact: As a Data Engineer, you will play a critical role in transforming raw data into valuable insights that drive business decisions. Your work will have a direct impact on the company's ability to make data-driven decisions and optimize its operations.

Growth Opportunities:

Technical skill development and specialization in emerging data technologies
Technical mentorship and leadership opportunities within the data engineering team
Architecture decision-making and technical leadership roles within the organization

📝 Enhancement Note: This role offers opportunities for growth in technical leadership and architecture decision-making, with a focus on data pipelines, cloud infrastructure, and reporting. The company provides a supportive environment for technical skill development and specialization in emerging data technologies.

🌐 Work Environment

Office Type: Hybrid (On-site and Remote) work environment, with flexible hours for deployment windows, maintenance, and project deadlines

Office Location(s): Quezon City, Metro Manila, Philippines

Workspace Context:

Collaborative workspaces with multiple monitors and testing devices available
Cross-functional interaction with data analysts, scientists, and business users to understand their data requirements and deliver relevant data solutions
Flexible work arrangements to accommodate deployment windows, maintenance, and project deadlines

Work Schedule: Full-time (40 hours per week), with flexible hours for deployment windows, maintenance, and project deadlines

📝 Enhancement Note: This role operates in a hybrid work environment, with flexible hours for deployment windows, maintenance, and project deadlines. The company provides collaborative workspaces with multiple monitors and testing devices available, as well as opportunities for cross-functional interaction with data analysts, scientists, and business users.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: A hands-on technical assessment to evaluate your data engineering skills, focusing on data pipeline development, cloud infrastructure, and reporting
Behavioral Interview: A behavioral interview to assess your problem-solving skills, communication, and collaboration abilities
Final Evaluation: A final evaluation to discuss your technical impact, career goals, and fit within the data engineering team

Portfolio Review Tips:

Highlight your ability to design, develop, and maintain data pipelines and infrastructure across hybrid cloud environments
Showcase your experience with reporting and visualization tools, such as Power BI, Tableau, or Looker
Demonstrate your problem-solving skills and ability to troubleshoot complex data-related issues

Technical Challenge Preparation:

Brush up on your data engineering skills, with a focus on data pipeline development, cloud infrastructure, and reporting
Familiarize yourself with the company's preferred cloud platform (e.g., AWS, Azure, GCP) and its data-related services
Prepare for behavioral interview questions that assess your problem-solving skills, communication, and collaboration abilities

ATS Keywords: Data Pipeline, ETL, Cloud Infrastructure, Hybrid Cloud, Data Quality, Data Governance, Data Modeling, Automation, Reporting, Visualization, Problem-Solving, Collaboration, SQL, Python, ETL Tools, Cloud Platforms

📝 Enhancement Note: The interview process for this role includes a hands-on technical assessment, a behavioral interview, and a final evaluation. Portfolio review tips and technical challenge preparation should focus on data pipeline development, cloud infrastructure, and reporting, as well as problem-solving skills and collaboration abilities.

🛠 Technology Stack & Web Infrastructure

Cloud Platforms:

Amazon Web Services (AWS)
Microsoft Azure
Google Cloud Platform (GCP)

Data Warehousing & Data Lake Technologies:

Google BigQuery
Amazon Redshift
Snowflake
Databricks

Data Pipeline & ETL Tools:

Apache Airflow
AWS Glue
Azure Data Factory
Google Cloud Dataflow

Reporting & Visualization Tools:

Power BI
Tableau
Looker

📝 Enhancement Note: This role requires experience with major cloud platforms (e.g., AWS, Azure, GCP) and their data-related services, as well as familiarity with data warehousing and data lake technologies (e.g., Google BigQuery, Amazon Redshift, Snowflake, Databricks). Proficiency in data pipeline and ETL tools, as well as reporting and visualization tools, is essential for success in this role.

👥 Team Culture & Values

Data Engineering Values:

Data-Driven: We make data-driven decisions to optimize our operations and drive business growth
Innovative: We embrace emerging technologies and continuously seek to improve our data engineering practices
Collaborative: We work closely with data analysts, scientists, and business users to understand their data requirements and deliver relevant data solutions
Quality-Focused: We prioritize data quality, governance, and compliance to ensure the accuracy and reliability of our data

Collaboration Style:

Cross-functional collaboration with data analysts, scientists, and business users to understand their data requirements and deliver relevant data solutions
Code review culture and peer programming practices to ensure the quality and maintainability of our data pipelines and infrastructure
Knowledge sharing, technical mentoring, and continuous learning to foster a culture of growth and development within the data engineering team

📝 Enhancement Note: The data engineering team at Octal Philippines Inc. values data-driven decision-making, innovation, collaboration, and quality. The team fosters a culture of cross-functional collaboration, code review, and continuous learning to ensure the delivery of high-quality data solutions.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Designing and implementing scalable data pipelines and infrastructure across hybrid cloud environments
Ensuring data quality, governance, and compliance through implementation of data validation, cleansing, and standardization processes
Troubleshooting complex data-related issues and finding innovative solutions

Learning & Development Opportunities:

Technical skill development and specialization in emerging data technologies
Conference attendance, certification, and community involvement to stay up-to-date with the latest trends and best practices in data engineering
Technical mentorship, leadership development, and architecture decision-making opportunities within the data engineering team

📝 Enhancement Note: This role presents technical challenges in designing and implementing scalable data pipelines and infrastructure, ensuring data quality and governance, and troubleshooting complex data-related issues. The company provides learning and development opportunities for technical skill development, conference attendance, and mentorship within the data engineering team.

💡 Interview Preparation

Technical Questions:

Data Pipeline Development: Can you walk us through your experience with designing, developing, and maintaining data pipelines and infrastructure across hybrid cloud environments? What tools and technologies have you used in the past?
Cloud Infrastructure: How have you managed and optimized data infrastructure across hybrid cloud environments? What challenges have you faced, and how did you overcome them?
Data Quality: Can you describe your approach to ensuring data quality through implementation of data validation, cleansing, and standardization processes? What tools and technologies have you used in the past?
Reporting and Visualization: How have you used reporting and visualization tools to create interactive dashboards and provide actionable insights to stakeholders? What challenges have you faced, and how did you overcome them?

Company & Culture Questions:

Data Engineering Team: How do you collaborate with data analysts, scientists, and business users to understand their data requirements and deliver relevant data solutions? What is your approach to cross-functional collaboration?
Data Governance: How do you ensure data governance, security, and compliance within your data engineering practices? What policies and procedures do you follow?
Data Modeling: Can you describe your approach to data modeling and data warehouse design? What techniques and tools have you used in the past?

Portfolio Presentation Strategy:

Highlight your ability to design, develop, and maintain data pipelines and infrastructure across hybrid cloud environments
Showcase your experience with reporting and visualization tools, such as Power BI, Tableau, or Looker
Demonstrate your problem-solving skills and ability to troubleshoot complex data-related issues

📝 Enhancement Note: The technical interview for this role focuses on data pipeline development, cloud infrastructure, data quality, and reporting and visualization. Company and culture questions assess your approach to cross-functional collaboration, data governance, and data modeling. Portfolio presentation strategy should highlight your ability to design, develop, and maintain data pipelines and infrastructure, as well as your experience with reporting and visualization tools and problem-solving skills.

📌 Application Steps

To apply for this Data Engineer (Hybrid Cloud, Reporting, and Visualization) position at Octal Philippines Inc.:

Customize Your Portfolio: Highlight your ability to design, develop, and maintain data pipelines and infrastructure across hybrid cloud environments, as well as your experience with reporting and visualization tools and problem-solving skills
Optimize Your Resume: Emphasize your relevant data engineering experience, skills, and qualifications, with a focus on data pipeline development, cloud infrastructure, and reporting
Prepare for Technical Interview: Brush up on your data engineering skills, with a focus on data pipeline development, cloud infrastructure, and reporting. Familiarize yourself with the company's preferred cloud platform and data-related services, as well as data governance and compliance policies and procedures
Research the Company: Understand Octal Philippines Inc.'s focus on data-driven decision-making, innovation, collaboration, and quality within the data engineering team. Prepare for behavioral interview questions that assess your problem-solving skills, communication, and collaboration abilities

⚠️ Important Notice: This enhanced job description includes AI-generated insights and data engineering industry-standard assumptions. All details should be verified directly with Octal Philippines Inc. before making application decisions.

Data Engineer (Hybrid Cloud, Reporting, and Visualization)