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Location
In office (Makati)
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 8, 2026. First seen by Alion on Oct 5, 2026. Octal Philippines scores C on the Alion truth index.

Overview
Company
Impact
Profile match
Octal Philippines is a technology outsourcing and staffing company based in Manila that supplies engineers, support specialists and back-office staff to clients in North America, Europe and Australia. Its work covers software development, quality assurance, technical support, data processing and customer operations, delivered from its own facilities in the Philippines under the offshore delivery model the country built its services industry on. It advertises client vacancies under its own name rather than the end employer's, which is why its board carries roles spanning several unrelated industries at once.

About the Role

We are hiring two Full-Stack Developers, Data Applications, who will develop the backend data pipelines and self-service application layer.

The developers will work across the same surface: Python pipelines, Snowflake workflows, REST API integrations, and web-based self-service applications that replace 40+ Alteryx Gallery apps used daily by five internal teams.

Our team operates in an AI-assisted development model. We use Claude (Anthropic) as an active co-author across the full engineering lifecycle, code generation, agentic task execution, architectural review, and documentation. This is not optional tooling. It is how we move fast with a lean team against a hard deadline.

Requirements

  • 7+ years of Python development - pandas, requests, openpyxl, regex as daily tools; comfortable owning a production codebase end to end
  • Solid Snowflake SQL - joins, CTEs, window functions, write operations (INSERT, MERGE, TRUNCATE/INSERT)
  • Data pipeline architecture - proven ability to design a pipeline from scratch: choose the right processing model (batch vs. event-driven), select appropriate AWS services, and defend those decisions; has produced architecture decisions that were adopted by a team, not just implemented someone else's design
  • REST API experience - OAuth2, pagination, rate limiting, JSON/XML parsing
  • Full-stack capability - Python backend (Flask or FastAPI) with HTML/JS frontend; able to build and ship a working web application end to end
  • AWS data pipeline architecture - hands-on experience selecting and configuring AWS services for a data workload from scratch: Lambda, Step Functions or Glue for orchestration, ECS/Fargate or EC2 for execution, S3 for storage, Secrets Manager for credential management, and EventBridge for scheduling; can justify which service to use and why for a given context
  • Demonstrated experience with AI-assisted development - using LLMs (Claude, Copilot, GPT-4, or equivalent) as active co-authors in a production engineering context, not just for autocomplete
  • Hands-on experience with agentic coding tools - Claude Code, Cursor, Devin, or similar - directing autonomous AI execution for real deliverables
  • Ability to reverse-engineer undocumented legacy workflows and reproduce their output exactly in a new stack - treating existing outputs as the test oracle
  • Git proficiency - branching, PRs, versioned releases
  • Production-scale pipeline experience - has owned a data pipeline serving multiple internal or external consumers, running on a defined schedule with SLA implications, and has debugged it in production; small or solo projects do not meet this bar
  • Strong Plus
  • Microsoft Graph API - SharePoint file writes, list operations, and email dispatch
  • Snowflake architecture - beyond querying: has designed table structures, configured roles and grants, managed compute sizing, or used cloning and time-travel in a production warehouse
  • Experience building self-service data tools or internal ops tooling for non-technical users
  • Familiarity with Alteryx Designer (understanding what you're replacing is a meaningful head start)
  • Workflow orchestration - Airflow, Prefect, or AWS Step Functions; has built and maintained DAGs with task dependencies, retry logic, and failure alerting in production
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