Data Engineer

IPinfo ipinfo.io Open · verified Oct 24, 2026

Remote Worldwide Full-time Mid-level Data Engineering

At a glance

Mid-level Data Engineering role at IPinfo. Remote · full-time.

Pay not stated Data Engineer salary

RoleData Engineering

SeniorityMid-level

LocationWorldwide

WorkplaceRemote

EmploymentFull-time

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Investigate inconsistent public and device datasets, then turn their contents into useful, dependable signals
Build and improve BigQuery pipelines with clear, efficient code, tests and end-to-end ownership
Use BigQuery’s geospatial capabilities to handle spatial data challenges, with occasional Python work
Document data problems, proposed fixes and decisions so teammates can reproduce and extend the work
Create web dashboards that make pipeline status visible to engineers and other colleagues

What you bring

You can write advanced SQL, including window functions and CTEs, and diagnose and improve slow queries
You have shown that you can make ambiguous, messy data reliable and explain the decisions behind your work
You have a substantial public record of data science or engineering work, or strong references for work on private code
You write maintainable code with tests and review, and can understand and stand behind AI-generated changes
You understand geospatial basics such as coordinate systems, spatial joins, containment and polygon operations

Who this fits

This mid-level, remote role suits someone comfortable working through unfamiliar data with limited hand-holding and taking ownership of pipelines and code. You’ll join a small team and communicate technical problems and decisions in clear, reproducible written records.

From the employer

Data Engineer

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.

What you'll do

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. Your job is to wade through these datasets, figure out what is going on, and extract a meaningful signal.
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, while dealing with the particular headaches and challenges that geospatial data poses. Occasionally working in python as well.
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; writing concise, reproducible records of the problem, the proposed solutions, and why you made the calls you did, so others can follow and build on them.
Work occasionally on web-based dashboards to provide visibility to our data pipelines for data engineers as well as others at the company.

What we're looking for

Must have

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.

Nice to have

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.

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