[Hiring] Senior Backend/Data Engineer @OneTeamAnywhere

Role Description

We are looking for a highly skilled Senior Backend / Data Engineer (Python · GCP · Vertex AI) to work on pi-sentiment — an existing Python-based sentiment analysis and social data pipeline on Google Cloud for a remote Europe-based client.

For this role, we only process candidates that are based in the Philippines and have legal authorization to work in the Philippines.

What You'll Do

• Core Responsibilities
  • Extend the Sentiment Pipeline:
    • Work within the existing end-to-end flow — Supabase RPC Data Scraping ingestion BigQuery Vertex AI Batch sentiment_predictions — adding features and fixing bugs without breaking what works.
  • Add & Maintain Platform Integrations:
    • Extend existing Apify-based adapters across Instagram, TikTok, Facebook, YouTube, LinkedIn, X/Twitter — handling auth, rate limits, schema drift, and backfills.
  • Ship Cloud Run Jobs:
    • Modify and add containerized Python jobs following existing patterns — SIGTERM handling, structured logging, idempotent retries.
  • Evolve Data Contracts:
    • Change BigQuery schemas and Supabase tables/RPCs without breaking the frontend; coordinate migrations with the frontend engineer.
  • Tune Models & Prompts:
    • Iterate on Gemini structured outputs (Pydantic schemas, enums) to keep sentiment and keyword extraction accurate across languages and platforms.
  • Benchmark & Evaluate:
    • Use the existing benchmarking suite to compare model configs on cost, latency, and quality.
  • Write Tests:
    • Add pytest coverage for your changes — unit, integration, E2E where warranted.

Qualifications

• Strong Python in production: type hints, Pydantic, pytest, clean module boundaries. Years matter less than evidence — show us code you've shipped.
• GCP under load: Cloud Run, BigQuery, Cloud Storage. You've operated it, not just prototyped.
• SQL that survives review: complex BigQuery or Postgres — window functions, partitioning, query optimization.
• LLM integration in production: you've shipped a feature backed by Vertex AI, OpenAI, or Anthropic, and you know what structured outputs and prompt regressions feel like.
• Cross-stack literacy: you can read a Next.js / TypeScript PR, understand what data it needs, and co-design the contract with our frontend engineer. Writing React is not required.
• Proactive operator: you drive your own work, flag blockers fast, and don't wait to be assigned.

Requirements

• Vertex AI Gemini specifically (Batch Prediction, structured JSON output with enums).
• Supabase / PostgreSQL with RLS, RPCs, migrations, multi-tenant patterns.
• Apify or similar ingestion platforms for social data.
• Data pipeline depth: idempotent backfills, schema evolution, cost engineering (BigQuery slots, batch vs. online).
• Docker (multi-stage, slim) with Cloud Run parity.
• Observability that isn't print(): structured logging, Cloud Logging, Sentry.
• GitHub Actions for CI/CD.
• Multilingual NLP experience (our comments span many languages).
• Terraform / IaC for GCP.

What This Role Is Not a Fit If...

• You need detailed specs for every task.
• You wait to be checked in on.
• You go silent when blocked.
• You expect a long onboarding ramp.

Technical Environment

• Core Technologies
  • Language: Python 3.11+ (strict typing, Pydantic v2).
  • ML / LLM: Vertex AI Gemini (2.5-flash) with structured JSON output.
  • Cloud: Google Cloud — Cloud Run Jobs, Cloud Scheduler, Cloud Storage, BigQuery, Vertex AI.
  • Region: europe-west3 (EU-focused).
• Data & Storage
  • Analytics warehouse: BigQuery (partitioned, clustered).
  • Operational DB: Supabase (PostgreSQL with RLS) — shared with the frontend.
  • Ingestion: Apify (15+ social platform adapters), Data365 API.
  • Batch ML: Vertex AI Batch Prediction (JSONL in/out via GCS).
• Developer Experience
  • Package Manager: uv / pip.
  • Formatting: black.
  • Linting: flake8.
  • Testing: pytest (unit, integration, E2E).
  • Secrets: dotenvx (encrypted environment files).
  • Containers: Docker Cloud Run Jobs.
  • Version Control: GitHub with trunk-based development.
  • Monitoring: Cloud Logging, Sentry.

What You Get

• Real ML in production: Gemini with real cost, latency, and quality trade-offs — not prototypes.
• End-to-end ownership: from ingestion to the Supabase row the frontend reads, the whole path is yours.
• A small team, no silos: one PO, one frontend engineer, you. Decisions are fast because the room is small.
• Remote and async: We don't care where you work or when, as long as you communicate and ship.
• Learning budget for conferences and courses.

Our Engineering Principles

• Type Safety First: Pydantic and type hints catch bugs at the boundary, not in production.
• Cost-Aware: Batch over online when it fits; measure before scaling.
• Observable: Structured logs, error tracking, and metrics ship with every job.
• Trunk-Based Development: Small, frequent PRs with feature flags over long-lived branches.

Interview Process

• Screen (15 min): Your background, what you've shipped, why this role.
• Take-home (5-6 hours): Small ingestion BigQuery LLM-enrichment task on GCP. AI-assisted development is fine — we care about the decisions, not the keystrokes.
• Code walk-through (60 min): Walk us through your solution. Expect pushback on trade-offs.
• Pairing session (60 min): Open a real pi-sentiment issue together. We want to see how you read unfamiliar code and where you ask questions.
• Offer: We move quickly for strong candidates.