What Makes SalesNow Different

Data scale: 8B data points, 14M records, 2.3M daily updates (vs. 1M-100M rows at typical companies)
Data ownership: You build and own Japan's largest corporate intelligence platform (vs. processing someone else's data)
AI integration: 20+ AI pipelines in production, MCP Server live (vs. "We're planning to use AI someday")
Dev tools: Claude Code MAX ($200/month), Cursor, CodeRabbit — all company-paid (vs. standard IDE, maybe Copilot)
Your code's impact: Feeds AI products used by enterprise clients, millisecond latency matters (vs. dashboards no one reads)
Career trajectory: Build Japan's data infrastructure → architect AI data products → global leverage (vs. same stack forever)
Remote work: 100% remote, no relocation required (vs. hybrid / office-required)

Why This Role Exists

AI models are commodities now. The cost of running GPT-3.5-equivalent models dropped 280x in two years. Every company has access to the same foundation models. The war moved to data — who owns it, who structures it, who feeds it to AI.
Oracle's Larry Ellison said it plainly on his December 2025 earnings call: "AI models are trained on the same public data, so they're rapidly commoditizing. AI inference on private data will be an even bigger, more valuable business." McKinsey echoes: "By 2030, the AI leaders will be defined not by who trained the biggest model, but by who built the most reliable systems on proprietary data."
SalesNow owns 14 million company records and 8 billion data points — the largest structured corporate intelligence platform in Japan. This data updates 2.3 million records daily, with differential refresh as fast as every 60 seconds. Hiring signals, funding rounds, organizational changes, press releases, job postings — all structured, all real-time, all proprietary.
This data doesn't exist inside any LLM's training set. The only way to access it is through SalesNow. That's the moat.
We need data engineers who can build and scale the pipelines that make this moat wider every day.

What You'll Build

This is not "maintain existing ETL jobs." You're building the data nervous system of Japan's corporate intelligence.
The Scale
- 14 million+ company records across Japan's entire business landscape — from Toyota to a 3-person startup in Okinawa
- 8 billion data points structured and queryable at millisecond speed
- 2.3 million records updated daily — hiring signals, funding rounds, organizational changes, press releases, job postings
- Sub-minute differential refresh — when a company posts a new job or announces funding, SalesNow knows within 60 seconds
- 42+ data sources feeding into a unified schema
- 5 delivery formats: Web app UI / CRM integration (Salesforce, HubSpot) / MCP Server / Data API / Custom AI Agents

AI-Native Development Culture

SalesNow doesn't just "use AI." AI is the operating system of how we build software. What this means for you, concretely:
- Claude Code MAX — Company-paid for every engineer. This is a $200/month tool that most individual developers can't justify. You get it Day 1, fully covered
- Monthly AI tool budget — Tens of thousands of yen per person, on top of Claude Code MAX, for any AI tool you want to try
- CodeRabbit — Automated PR reviews powered by AI. Every pull request gets AI review before human review
- Vibe coding culture — Not just engineers. The CEO, COO, and business teams all write code with AI assistance. When you propose a technical solution, leadership actually understands it
- 20+ AI pipelines in production — X post generation, candidate screening, PR monitoring, behavioral analytics, financial page generation — all running daily in production. This isn't a demo. It's how the company operates
- No "AI committee" or "innovation lab" gatekeeping — You want to try a new approach? Ship it. The decision loop is measured in hours, not quarters
Why this matters for your career: In 3 years, every engineering role will require AI-native development skills. At SalesNow, you build those skills now — not by watching tutorials, but by shipping production AI systems daily.

Must Have

2+ years of professional data engineering experience
Strong Python skills — clean, testable pipeline code
Strong SQL skills — complex queries, window functions, query optimization
Experience with at least one orchestration tool — Airflow, Dagster, Prefect, or equivalent
Experience with relational databases at scale — PostgreSQL preferred
English proficiency (professional working level)

Nice to Have

Language Policy

Engineering team language: English. Code, PRs, technical docs, standups — all in English
Cross-team communication: AI-assisted. SalesNow provides professional AI translation tools (company-paid, ~$75/month per person) for any communication that crosses the language boundary. You will never be blocked by language
Japanese is a growth accelerator, not a gate. If you speak Japanese, it amplifies your impact. If you don't, the AI bridge ensures you're fully productive from Day 1

Before You Apply — Quick Self-Check

Please confirm the following before submitting your application:
- I have professional experience building data pipelines or data processing systems with Python
- I have professional experience writing SQL queries and working with databases
- I am available for full-time remote work (40 hours/week)
Bonus: Japanese language ability (JLPT N3+) is a strong plus.

In Your Cover Letter, Please Include:

The largest dataset or data pipeline you've worked with — how many records per day, and what did it do?
What was the most technically challenging problem you solved in your career? (1-2 sentences is fine)
Which data tools or frameworks have you used? (e.g., Airflow, dbt, Spark, Kafka, Elasticsearch — list any that apply)