Senior Data Engineer

iScale Solutions; Makati; On-site; PHP 900,000 - 1,500,000; Full time

Benefits offered by this job

Competitive salary

Leave credits

Health coverage

SSS/PhilHealth/Pag-IBIG

Training & mentorship

Team activities

Modern tools

Career growth

Inclusive culture

Job summary

iScale Solutions is seeking a Data Engineer to design, implement, and maintain large-scale data pipelines in a cloud environment, focusing on reliability, scalability, and governance. You will collaborate with analytics, product, engineering, and data science teams to deliver data-driven solutions.

The role requires strong SQL, Hive, Python, and AWS skills, plus experience with Airflow, Spark, and data quality practices.

Qualifications

• 5+ years of professional experience in data engineering with ownership of production data pipelines.
• Strong SQL expertise with complex joins, window functions, transformations, and troubleshooting.
• Proficiency in Python for data processing, automation, integration, and production code.
• Hands-on experience with Amazon S3, Athena, EC2, and EMR.
• Strong experience with Apache Airflow for ETL/workflow orchestration.
• Experience implementing data quality checks, monitoring, and operational support processes.
• Ability to translate business requirements into technical designs with minimal supervision.
• Strong documentation and communication skills for stakeholders.
• Experience using Jira and Confluence for task tracking and documentation.
• Experience mentoring engineers or leading small teams is a plus.

Responsibilities

• Own data pipeline development and maintenance across ingestion, transformation, and delivery of large datasets.
• Lead technical design including data models, integration approaches, and workflow dependencies.
• Develop and optimize data processing with SQL, Hive, and Python; apply Spark where appropriate.
• Build and manage AWS workflows using S3, Athena, EC2, and EMR with monitoring and optimization.
• Orchestrate automated workflows with Airflow, including scheduling, retries, and recovery.
• Monitor pipeline reliability and data freshness; perform root cause analysis and improvements.
• Maintain data quality, governance, lineage documentation, and access controls.
• Lead a 2-3 engineer team; review code and designs, assist troubleshooting, track delivery.
• Collaborate with analytics, product, engineering, and data science teams; manage dependencies.
• Create and update solution designs, data flow diagrams, and data dictionaries in Confluence.
• Apply AI to engineering workflows; validate AI outputs before production use.
• Support agile delivery with Jira; participate in sprints and cross-region coordination.

Skills

SQL

Hive

Python

Spark

Airflow

Data modeling

AWS

Tools

Snowflake

OpenAI Codex

Snowflake Cortex

PySpark

Jira

Confluence

OpenAI Codex

Job description

Own data pipeline development: Design, develop, and maintain reliable ETL/ELT pipelines for ingesting, transforming, and delivering large-scale datasets across multiple platforms and sources.

Lead technical design: Define processing logic, data models, integration approaches, and workflow dependencies, considering scalability, maintainability, security, and cost.

Develop and optimize data processing: Write efficient SQL, Hive queries, and Python code. Apply distributed processing frameworks such as Spark where appropriate.

Build and manage AWS workflows: Develop and support data solutions using Amazon S3, Athena, EC2, and EMR, including monitoring and resource optimization.

Manage workflow orchestration: Build and maintain automated workflows using Apache Airflow or comparable orchestration tools, including scheduling, dependencies, retries, alerting, and recovery.

Ensure production reliability: Monitor pipeline performance and data freshness, investigate failures, perform root cause analysis, and implement improvements that prevent recurring issues.

Maintain data quality and governance : Establish validation checks, reconciliation processes, lineage documentation, and appropriate access controls throughout the data lifecycle.

Lead and mentor a small team: Guide 2-3 engineers, break requirements into actionable tasks, review code and designs, support troubleshooting, and track delivery commitments.

Collaborate with stakeholders: Work closely with analytics, product, engineering, and data science teams to clarify requirements, agree on priorities, communicate progress, and manage dependencies.

Maintain technical documentation: Create and update solution designs, data flow diagrams, data dictionaries, operating procedures, and support runbooks using Confluence or similar platforms.

Apply AI to engineering workflows: Identify practical opportunities to use AI for development, documentation, troubleshooting, and automation. Build simple AI agents and validate their outputs before operational use.

Support agile delivery: Manage work through Jira, contribute to sprint planning and technical reviews, and coordinate across regions and work schedules as required.

Requirements

Requirements:

5+ years of professional experience in data engineering, with demonstrated ownership of production data pipelines and large-scale data processing.

Strong SQL expertise, including complex joins, window functions, transformations, query tuning, and troubleshooting.

Strong working knowledge of Hive and the Hadoop ecosystem, including partitioning, storage formats, and distributed data processing concepts.

Proficiency in Python for data processing, automation, integration, and maintainable production code.

Hands-on experience with Amazon S3, Athena, EC2, and EMR.

Strong experience with Apache Airflow or another ETL/workflow orchestration tool, with Airflow preferred.

Experience implementing data quality checks, monitoring, error handling, and operational support processes.

Ability to translate business requirements into technical designs and deliver solutions with minimal supervision.

Strong documentation and communication skills, including the ability to explain technical issues clearly to business and technical stakeholders.

Working experience with Jira and Confluence for task tracking, delivery coordination, and documentation.

Practical understanding of AI capabilities and limitations, along with hands-on experience creating simple AI agents or AI-assisted automations.

Experience with version control, code reviews, and collaborative development practices.

Preferred Qualifications

Hands-on experience with Apache Spark using PySpark or Scala.

Experience with Snowflake, including data transformation, query optimization, and integration with cloud data pipelines.

Practical experience using OpenAI Codex for engineering tasks and Snowflake Cortex for AI-enabled data workflows.

Previous experience mentoring engineers, leading technical delivery, or managing a small team. Formal people management experience is an added advantage.

Experience supporting marketing technology, audience data, campaign reporting, or customer analytics.

What we offer:

Competitive Salary Package: Receive a pay package that matches your skills and experience.

Vacation and Sick Leave credits: Enjoy vacation and sick leave credits to maintain work-life balance.

Health Coverage: Get medical, dental, and vision insurance for you and your dependents.

Government-Mandated Benefits: Full coverage of all statutory benefits like SSS, PhilHealth, and Pag-IBIG.

Learning Opportunities: Access training, certifications, and mentorship to grow your career.

Team Engagement: Join team-building activities and wellness programs.

Modern Tools: Use the latest technology to excel in your role.

Career Growth: Clear paths for promotion and professional development.

Inclusive Culture: Be part of a diverse, supportive, and collaborative global team.