Senior Data Engineer

WeSupport Incorporated; Taguig; Hybrid; PHP 2,232,000 - 3,906,000; Full time

Job summary

WeSupport Incorporated is seeking a Senior Data Engineer to design, build, and optimize automated data pipelines, ETL/ELT processes, and data models across cloud platforms. You will partner with business, analytics, and product teams to translate data requirements into scalable technical solutions that support strategic initiatives.

The role requires 8+ years in data engineering, expertise in modern cloud data ecosystems (Databricks, Snowflake, Azure), and strong communication and leadership

Qualifications

• 8+ years of total Data Engineering experience focusing on large-scale pipelines.
• Experience with ETL/ELT development on cloud platforms.
• Proficiency in Python and SQL for data transformation and queries.
• Familiarity with Databricks or Snowflake.
• Strong communication, leadership, and stakeholder collaboration.

Responsibilities

• Design, build, and optimize data pipelines and models on cloud platforms.
• Lead data migration initiatives with minimal business disruption.
• Develop and maintain ETL/ELT workflows across sources.
• Collaborate with product and analytics teams to meet requirements.
• Document workflows, schemas, and runbooks; ensure governance and security.
• Monitor pipeline health and resolve data incidents.

Skills

Data engineering

Leadership

Communication

Problem solving

Stakeholder management

Tools

Databricks

Snowflake

Azure Data Factory

Azure Synapse

Azure Storage

Job description

About the role

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. Partner with business, analytics, and product teams to translate data requirements into effective technical solutions that support strategic initiatives.

Qualifications

• 8 years or above 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
• Experience with cloud services, preferably Microsoft Azure (e.g., Azure Data Factory, Azure Synapse, Azure Storage, etc.)
• Strong verbal and written communication skills
• Demonstrated leadership and technical influence
• Strong analytical, critical thinking, and problem-solving abilities
• Stakeholder and cross‑functional collaboration skills

Key responsibilities

• 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