About the job
Data Acquisition EngineerWe're looking for a Data Acquisition Engineer who can own how external data flows from the internet into our product: collecting it, structuring it, and automating the pipelines behind it.
Company
Eighty Days is building a new way to discover the world centred on taste, trust, and personal context. Today, travel discovery is fragmented across social media, search, and private conversations. We are creating a product that brings this together into a structured, map-based experience where recommendations are attributed, meaningful, and built over time.Our focus is not just on helping people plan trips, but on becoming the place where they continuously save, organise, and revisit places that matter.Website: https://eightydays.ai/
What you'll do
- Identify and evaluate data sources (websites, public APIs, and app endpoints) and decide what's worth collecting.
- Build Python data extraction services that reliably pull structured data from static HTML, JSON endpoints, and JavaScript-rendered pages.
- Keep collection running smoothly as sources change, throttle requests, or restrict access, using proxies, sessions, retries, and backoff.
- Normalize messy, inconsistent output into a single clean schema: addresses, timings, categories, coordinates, prices.
- Deduplicate places across sources where the same venue appears under different names and coordinates.
- Automate the pipeline end to end with scheduled, repeatable runs and no manual steps.
- Validate data before it reaches production, and build the monitoring that catches problems before users do.
What we're looking for
- 2-4 years building data extraction services and pipelines that ran in production.
- Strong Python, including asyncio and concurrency control.
- Experience with Playwright, Selenium, or similar tools for JavaScript-heavy pages, alongside plain HTTP-based data collection.
- Practical experience working within rate limits and access controls: proxies, headers, backoff.
- MongoDB or strong SQL: aggregation, indexing, bulk writes.
- Comfortable with Docker, Linux, Git, and AWS (S3, SQS, EC2).
- Ability to take a source from exploration, to a working collector, to structured data, to an automated production pipeline.
- Genuine attention to data quality: you verify output before you trust it.
Bonus points
- Experience building data pipelines for consumer or location-based products.
- Familiarity with geospatial data and place matching across sources.
- Experience with workflow orchestration tools (Airflow, Prefect, or similar).
- Great to have worked in a fast paced product-based startup.