Role Description At Umpisa Inc., our mission is to make the Philippines be known globally as a tech hub.  Umpisa Inc. is a progressive technology services company that partners with select industries, clients and people to work on pioneering and industry-changing solutions via digital transformation, modern software development and venture building. We create a set of world-class and impactful products and solutions to help organizations and individuals live better lives. We offer demanding, challenging and rewarding careers in software development, product development, emerging technologies, and more for the right candidates. Essential Skills: - Aligns with our values: Excellence, Integrity, Professionalism, People Success, Customer Success, Fun, Innovation and Diversity - Strong communication skills - Strong problem solving and analytical skills - Excellent problem-solving ability - Would like to work as part of a self-organizing Scrum team in a scaled agile framework - Must be a self-starter and loves to collaborate with the team and client Job Summary We are seeking a talented and experienced Data Engineer specializing in Google Cloud Platform (GCP) ETL tools to join our team. As a Data Engineer you will be instrumental in designing, implementing, and maintaining scalable data pipelines and systems leveraging GCP's suite of ETL tools. Responsibilities - Design, develop, and maintain scalable data pipelines using GCP ETL tools like Cloud Dataflow, Dataprep, or Dataproc - Collaborate with cross-functional teams to understand data requirements and implement solutions using GCP services to support data-driven initiatives - Optimize data models and architecture for performance and reliability within GCP - Build and maintain data warehouses, data lakes, and other data storage solutions on Google Cloud Platform - Ensure data quality, integrity, and security across various datasets within GCP - Identify and resolve performance bottlenecks in data infrastructure specifically related to GCP ETL tools - Work closely with data scientists, analysts, and other stakeholders to support their data needs within the GCP ecosystem - Stay updated with the latest GCP technologies and trends in data engineering and implement best practices

• Report to our Engineering Manager • Define and own the long-term data architecture strategy for Unqork's core platform, covering data modeling, query design, storage topology, and access patterns across a complex data environment that includes MongoDB as well as integration with Relational and Columnar database models • Ensure the data layer meets the security, performance, scalability, and resilience requirements of enterprise-grade, mission-critical applications. • Evaluate and recommend the right database technologies, indexing strategies, caching layers, ETL and search infrastructure for each class of workload — including when to use MongoDB Atlas Search, Caching (e.g., Redis), Querying (e.g., Kafka) and Streaming components • Own the end-to-end solutions around data transfers using solutions like ETL for customers. • Own the data architecture for Unqork's AI-driven development layer — defining persistence, versioning, and query standards for AI-generated configurations. • Lead the design of declarative data models and schemas that enable non-technical users to build complex logic while maintaining strict data integrity. • Define the architectural boundary between database-layer computation (aggregation pipelines, indexing) and application-layer computation (Node.js post-processing, in-memory caching), and establish standards for which work belongs where. • Create and maintain comprehensive documentation including data architecture blueprints, indexing governance policies, query standards, and migration playbooks. • Own capacity planning and cost modeling for data infrastructure resources as Unqork scales. • Mentor and grow a team of data engineers and database engineers responsible for the health and performance of Unqork's data platform. • Establish data modeling best practices and enforce standardization across all environments — including schema conventions, index lifecycle management, and pagination contract design. • Oversee the design and operation of Unqork's database infrastructure — defining thresholds, coverage policies, write amplification limits, and manual override processes. • Drive data operational excellence by implementing and refining query performance monitoring, slow query alerting, explain plan review processes, and incident response playbooks for database degradation events. • Define and enforce data access governance — including RBAC data model standards, cache TTL policies, and the rules under which eventual consistency is acceptable vs. when strong consistency is required. • Comply with security regulations while working on data designs and patterns for Unqork platform. • Partner with Product to translate product requirements into data model decisions, and identify where relaxing a product constraint unlocks a disproportionate architectural improvement.

• Lead a team that owns the AI self-service portal and the related set of microservices. • Work with a cross-functional team of engineers to contribute to our data platform. • Build and support a distributed platform supporting all ExParte data. • Work across our product, primarily on the data pipelines. • Interface directly with internal teams. • Evaluate software and implementation options and document them for technical teams. • Work with data analysts to collect insight on possible data collection efficiencies and identify automation potential for manual workflows. • Integrate best qualitative practices in program design and development. • Be a part of a distributed team (we’re in North America and Europe). • Work with Azure cloud and Databricks. • Develop technical architectures and specific implementations to meet business needs. • Guide the team’s software engineering best practices by documenting standards and completing code reviews. • Troubleshoot new and existing code and provide feedback and solutions to structural issues in the codebase as they arise. • Advise on the feasibility of nonfunctional requirements and ensure the successful implementation of features while meeting those requirements.

• Work directly with product owners and data experts to build products that solve complex client problems • Build and support a distributed platform supporting all ExParte data • Work across our product, primarily on the data pipelines • Interface directly with internal teams • Evaluate software and implementation options and document them for technical teams • Work with data analysts to collect insight on possible data collection efficiencies and identify automation potential for manual workflows • Integrate best qualitative practices in program design and development. • Be a part of a distributed team (we’re in North America and Europe) • Work with Azure cloud and Databricks • Develop technical architectures and specific implementations to meet business needs.