Data Engineer

Responsibilities

• Build and maintain integrations with a large and growing number of external data sources — APIs, WebSockets, file drops, streams, scrapers, and formats you haven't seen yet
• Evaluate and compare data vendors across quality, reliability, coverage, cost, and terms of service
• Negotiate contracts and manage commercial relationships with data providers
• Design and operate high-throughput ingestion pipelines handling mixed workloads (real-time, near-real-time, batch, event-driven)
• Build monitoring that tells you — before anyone else — when data is late, wrong, incomplete, or drifting
• Manage data quality at scale: anomaly detection, cross-source validation, schema drift detection, gap filling
• Handle both structured data (time-series, tabular) and unstructured data (documents, text, images) with appropriate extraction and storage
• Track costs per source, usage per consumer, and ROI — recommend what to keep, upgrade, or cancel
• Build tooling that makes adding the next data source faster than the last one
• Use AI tools aggressively in your daily work — for code generation, testing, documentation, anomaly analysis, and anything else that makes you faster

Requirements

**You've done this before:**
- 5+ years building data pipelines that run in production, 24/7, with real SLAs
- Deep hands-on experience with SQL databases and time-series data
- Python as your primary language, comfortable with async programming
- You've integrated with dozens of external APIs and dealt with the reality of unreliable vendors, changing schemas, rate limits, and bad documentation
- You've built monitoring and alerting for data systems — not as an afterthought but as part of how you work

**You think about the whole picture:**
- You don't just connect to an API. You think about what happens when it goes down, when the schema changes, when the data is wrong, when the bill doubles
- You understand that data has a cost and a value, and not every source is worth keeping
- You've worked with data vendors commercially — contracts, pricing tiers, usage negotiations

**You use AI daily:**
- AI coding tools are part of your workflow today, not something you're curious about
- You can articulate specifically how AI makes you faster and where it doesn't help
- You'd be frustrated if you couldn't use AI in your work

Nice to have

• Experience with financial or crypto market data
• Experience with streaming systems (Kafka or similar) at scale
• Vector database or embedding pipeline experience
• Experience with unstructured data extraction (PDFs, documents, NLP)

Perks & Benefits

• Senior individual contributor role with full ownership of the data domain
• Direct access to leadership — no bureaucracy, fast decisions
• AI tools provided and encouraged across all work
• Remote-friendly, async-first
• Compensation commensurate with experience