Looker deployments tend to drift over time as more teams request new dashboards, filters, and permissions, and someone eventually has to own the whole thing before it turns into a mess. That someone, in this case, is a Remote Looker Data Analyst working full-time with a fully distributed team. There is no office involved and no location requirement; the position is open worldwide.
SaaS tool specialist roles like this one exist because most companies buy a platform, roll it out to a handful of teams, and then never assign clear ownership of how it grows from there. A few years in, permissions are inconsistent, half the saved dashboards are duplicates, and nobody is quite sure which metric definition is the current one. This role exists to fix that pattern and then keep it from happening again, rather than doing a one-time cleanup and moving on.
SaaS tool specialists like this often become the informal go-to person for a whole category of questions well beyond their official job description, simply because they understand how the platform actually behaves under real use.
The salary attached to this role is 81,500 dollars annually. Along with that, the package includes:
That last one is worth using if you want to go deeper into Looker's more advanced features over time, and it is renewed annually rather than offered once.
Day to day, the role centers on keeping Looker running cleanly for the people who rely on it. That includes:
Much of the job is quiet, methodical work. A dashboard that looks fine in isolation can pull from three different sources with three different definitions of an active user, and part of the role is catching that before someone builds a quarterly report on top of it. A typical week might include cleaning up permissions after a reorg, walking a new hire through how to build their first explore, and quietly retiring a dashboard nobody has opened in months. One recurring headache is a metric that gets defined slightly differently in two places, say a marketing explore counting a trial signup one way and a finance explore counting it another.
Tracking that down and getting both sides to agree on a single definition is unglamorous work, but it is the kind of fix that prevents a much bigger argument in a leadership meeting later. Onboarding a new hire onto Looker usually takes an hour-long walkthrough followed by a week or two of answering small follow-up questions as they build their first few real explores.
A bachelor's degree in marketing, business, or a related field is expected, along with 24 months of hands-on experience configuring and managing Looker specifically. Platform certification is a plus where you already have it, and problem-solving ability matters more than any single credential.
Someone coming from a broader business intelligence background can usually pick up Looker's specific quirks within a month or two, as long as the underlying data modeling instincts are already solid. Experience with a modeling language like LookML specifically is a genuine advantage over general dashboard-building skills learned in a different tool, since so much of the platform's power sits in that layer rather than in the visual explore builder.
Because the work touches teams across the business, from marketing to finance to operations, communication matters as much as the technical configuration itself. You will spend real time explaining data decisions to people who are not analysts themselves, so the ability to translate a technical limitation into plain language is part of the job, not an extra add-on. Explaining to a sales director why a number moved without sounding defensive about it is a skill in its own right.
Remote work here runs on a normal working-hours schedule rather than a heavily synchronous one. Meetings are kept deliberately light, most coordination happens through chat and shared documentation, and the team is spread across enough time zones that async updates are the default rather than the exception. Remoteroles posts a fair number of platform-specialist roles like this one, and this employer in particular leans toward a low-meeting, high-documentation culture. Expect a short weekly sync with the wider data team and otherwise long, uninterrupted stretches to actually build and troubleshoot.
Working hours are close to a standard business-hours pattern, with enough flexibility that an appointment or a slow morning does not require a formal request, as long as the work itself gets done.
Growth from this role tends to run toward a broader analytics engineering or BI leadership position, especially at a company still figuring out how it wants to structure its data team. That trajectory is not guaranteed, but it comes up often enough that it is worth mentioning to anyone thinking about this as a multi-year move rather than a stopgap. The interview process usually includes a practical exercise, working through a sample data model or explaining how you would troubleshoot a broken dashboard, since that mirrors the actual work far better than a generic conversation about experience would.
People who settle into this role well tend to have a background split between analytics and platform administration, comfortable being the person other teams come to when a report looks wrong. If that describes your last couple of years of work, this is worth a look.
To apply, submit a resume along with a short description of a Looker deployment or dashboard project you have managed. Candidates who move forward can expect a short technical conversation as the next step, usually scheduled within a week or two of applying.