Data Engineer

Role Description

This is a demanding role on a small, high-leverage team. You'll be one of a handful of people responsible for the data behind IPinfo's location and context products, working on ambiguous problems with messy inputs and owning your pipelines end to end - including understanding every line you ship.

• Make sense of large, unfamiliar datasets sourced from publicly-contributed (and therefore inconsistent) datasets like OpenStreetMap and Overture, as well as error-prone device datasets with sometimes dozens of poorly-documented columns.
• Maintain and extend BigQuery data pipelines, writing efficient, transparent code that achieves complex data tasks while avoiding bloat and spaghetti.
• Work with particular expertise on Geospatial data, knowing the suite of BigQuery geospatial tools like the back of your hand.
• Use AI tooling to move quickly while fully owning every line in your PRs.
• Communicate problems and solutions clearly using our internal issue-tracking platform.
• Work occasionally on web-based dashboards to provide visibility to our data pipelines for data engineers as well as others at the company.

Qualifications

• Advanced SQL - window functions, CTEs, query restructuring for performance, and an understanding of why a query is slow and how to fix it. BigQuery is a strong plus.
• Strong communication skills - you know how to talk and write about complex problems and data pipelines productively.
• A track record of turning messy, ambiguous data into reliable, interpretable signals, with the judgment to explain your calls.
• An internet record of significant experience as a data scientist or engineer, on Github, StackOverflow, in the academic literature or on a personal blog, or strong references to back up a track record on proprietary code bases.
• Clean-code discipline: you don't ship code without tests, code review, readable abstractions. You prefer subtractive solutions to additive solutions.
• Fast learning - comfort becoming productive in unfamiliar domains (internet measurement, geospatial reasoning, internal tooling) with little hand-holding.
• AI-assisted development paired with full ownership - you can read, debug, and defend everything the tools produce.
• Geospatial fundamentals: coordinate systems, spatial joins, containment, polygon operations.

Requirements

• Cloud tooling and workflow orchestration (CI/CD, Docker, Airflow, etc.).
• JavaScript and web dashboards (e.g. Retool, Mapbox, internal validation and visualization tooling).
• Exposure to the science of internet measurement: BGP/ASN, rDNS, RTT-based geolocation, CGNAT, mobile vs. fixed-line IP behavior, geofeeds.
• Strong Python for geospatial data work - comfortable with the data and geospatial stack (pandas, geopandas, shapely) and writing code that holds up in production, not just in a notebook.