Data Engineer

Pointwest Squad Inc; Taguig; On-site; PHP 1,200,000 - 2,000,000; Full time

Job summary

Pointwest Squad Inc seeks a Senior Data Engineer to design, build, and optimize large-scale data pipelines in cloud environments. You will own ETL/ELT development, data models, and the reliability of data platforms.

Collaborate with business, analytics, and product teams to translate data requirements into scalable technical solutions that support strategic initiatives. Advanced Python and SQL skills, experience with Databricks and Snowflake, and a strong grasp of cloud data architectures are

Qualifications

• 8+ years of total Data Engineering experience with large-scale pipelines
• Proven experience designing, building, and optimizing scalable data solutions
• Strong hands-on Python for data transformation and automation
• Strong SQL for complex queries, data modeling, and performance tuning
• Experience with modern data cloud platforms (Databricks and/or Snowflake)
• Understanding cloud-native data architecture and governance practices

Responsibilities

• Design, build, and optimize automated data pipelines, ETL/ELT processes, and data models in cloud platforms.
• Support large-scale data migration initiatives with data accuracy and minimal business disruption.
• Develop and maintain ETL/ELT workflows for multiple sources with scalability and reliability.
• Collaborate with analytics and product teams to translate data requirements into technical solutions.
• Design and deliver data marts and customized extractions for reporting needs.
• Ensure compliance with data governance, security, and regulatory standards; monitor pipelines and troubleshoot incidents.

Skills

ETL/ELT

Python

SQL

Databricks

Snowflake

Cloud platforms

Job description

• Design, build, and optimize automated data pipelines, ETL/ELT processes, and data models to ingest, process, and store large volumes of data within cloud-based platforms.
• Support large-scale data migration initiatives, ensuring data accuracy, performance efficiency, and minimal business disruption.
• Develop and maintain ETL/ELT workflows to ingest, transform, and load data from multiple internal and external sources with a focus on scalability and reliability.
• Partner with business, analytics, and product teams to translate data requirements into effective technical solutions that support strategic initiatives.
• Design and deliver data marts and customized data extractions aligned with business and reporting needs.
• Ensure compliance with enterprise data governance, security, and regulatory standards. Monitor data pipeline health and performance, troubleshoot data incidents, and implement preventive and corrective measures.
• Document data workflows, schemas, technical specifications, and operational runbooks to support operational stability and knowledge transfer.
• Collaborate closely with product owners, data architects, and data scientists to maintain a reliable and efficient data infrastructure.
• Drive continuous improvement of data engineering practices, tools, and automation frameworks.
• Provide technical guidance and mentorship to junior engineers through code reviews, best-practice sharing, and troubleshooting support.
• Manage and deliver multiple data engineering initiatives concurrently while meeting quality, scope, and timeline expectations.

Requirements:

• At least 8 years of total Data Engineering experience, with strong exposure to large-scale data pipelines, ETL/ELT development, and enterprise or cloud-based data platforms.
• Proven experience designing, building, and optimizing scalable data solutions in modern data environments.
• Python – At least 4 out of 5 proficiency level, with strong hands‑on experience in data transformation, automation, and pipeline development.
• SQL – At least 3 out of 5 proficiency level, with demonstrated capability in complex queries, data modeling, and performance tuning.
• Experience working with modern data cloud platforms, such as Databricks and/or Snowflake.
• Platform and Cloud Exposure - Currently working with or has recent hands-on experience in enterprise-grade cloud data ecosystems. With a strong understanding of cloud-native data architecture and best practices.