Senior Data Analyst

Natlink; Finland; Hybrid; EUR 70,000 - 95,000; Full time

Benefits offered by this job

Office in Kempele (Finland)

Remote-friendly arrangement

Job summary

Natlink in Finland seeks a Senior Data Analyst to bridge the data platform with the business. You’ll build self-serve dashboards in Lightdash, collaborate with support, marketing, product, finance and sales, and define the questions the company should answer.

You’ll work across our BigQuery/dbt stack, push analytic models, and mentor the team as we scale. The role combines deep analysis with enabling others to answer their own questions, in a fast, outdoors-connected company.

Qualifications

• Demonstrated impact: analysis that changed decisions with walk-throughs.
• Built BI or semantic layer used by non-technical people independently.
• Strong SQL and comfort with a dbt-modelled warehouse.

Responsibilities

• Build self-serve dashboards and explores in Lightdash that teams use daily.
• Collaborate with support, marketing, product, commercial and finance to understand needs.
• Identify recurring questions and make them answerable without you.
• Feed definitions back to the analytics engineer to grow the data model.

Skills

Advanced SQL

dbt

Data analysis

Lightdash / BI tooling

Stakeholder communication

AI tooling / coding agents

Tools

BigQuery

Lightdash

dbt

Job description

Senior Data Analyst- Join Our Growing Team

Location: Oulu (Kempele) or Remote Finland

Natlink builds technology for coordination and response. A hunting team spread across several kilometres of forest. A dog working a scent line out of sight of its handler. Someone collapsing from cardiac arrest in a Swedish town, and the nearest trained volunteer needing to reach them before the ambulance can.

Underneath, it’s the same problem: get the right people to the right place quickly, usually outdoors, often where the network barely works. Our services are used by close to a million Europeans, through WeHunt, Tracker GPS devices, Burrel cameras and Heartrunner.

Why this role exists

We spent the past 6-months building the data platform: BigQuery, dbt and Lightdash. It's live and the company runs on it. What we don't have yet is the layer between that platform and the people who need to use it.

Right now, most teams still have to come to us. That's the constraint. The job is to close it, in two directions at once: build the things in Lightdash that let support, marketing, product, commercial and finance answer their own questions, and do the deep analytical work that nobody in those teams could do for themselves.

You'd be the most senior analyst here, working across the whole business rather than inside one function.

The team

Four people, reporting to the Head of Data. A data engineer on the platform, an analytics engineer on the model layer and the definitions behind Lightdash, and you on everything the business actually touches. Natlink is around a hundred people and backed by Verdane, so the data function is small, visible and expected to matter.

What you’ll work on

Making teams self-sufficient. This is the larger half of the job.

• Building the explores, dashboards and self-serve surfaces in Lightdash that teams use daily without asking you first
• Sitting with support, marketing, product, commercial and finance often enough to know how they actually think, what they're deciding, and where they get stuck
• Working out which questions each team asks repeatedly, and making those answerable without you
• Feeding definitions and gaps back to the analytics engineer, so the model layer grows toward what the business needs

Deep analysis nobody else can do.

• Retention and subscription behaviour, including predictive work
• Customer value across hardware and subscription together, which is harder than either alone
• How teams and communities use the products, which is social data rather than individual data
• Marketing effectiveness across markets
• What support conversations reveal about churn before it appears in the numbers

The data

Most analyst roles offer clicks and transactions. You get those, and a good deal more.

Continuous positioning telemetry from devices working in real terrain. Behaviour from groups coordinating together rather than individuals using an app alone. Hardware and subscription revenue inside a single customer relationship. Trail camera data from remote sites. Emergency response data where the outcome variable is how quickly someone reached a person in cardiac arrest. Support and marketing across several markets with different cultures, seasons and regulations, and demand that swings hard enough each year to break most off-the-shelf models.

It's an unusually varied set for a company this size, and very little of it has been properly analysed. That's the opportunity.

What you’d grow into

Raising the analytical standard as the team grows. The formal data literacy work across the company sits with the Head of Data for now, and you’ll take an increasing share of it over time as the self-serve foundation you’re building makes it possible.

How we work

Priorities are set with the leadership team through our data steering group, led by the Head of Data. You work on what the company has agreed matters, and you’re expected to argue when you think it has that wrong. This is not a ticket queue.

Numbers that leave the building come from the data platform, not from source systems. We don’t publish figures we can’t reconcile or trust.

We’re deliberately AI native. Claude Code and Cursor daily, conventions files in every repo, agents and automated flows in production. It’s why a team this size can support a company this size. What matters is how you review and challenge what an agent produces, not how fast you can generate something.

What you need on day one

• A track record of analysis that changed decisions rather than reported on them, and two or three examples you can walk us through
• You’ve built something in a BI or semantic layer that non-technical people genuinely used on their own, and you can say what made it work when so much self-serve doesn’t
• Strong SQL, and comfort working inside a dbt-modelled warehouse
• The judgment to know when statistical or predictive modelling is warranted and when it isn’t
• Credible in the room with product, marketing, commercial and finance, including at executive level
• You already work with AI coding agents every day

Nice to have

• Experience from a subscription business
• Geospatial or sensor data
• Swedish, Finnish or Norwegian, since much of the source data and the user base is
• An interest in the outdoors, dogs, or the emergency response side of what we do

You’ll fit if

You’d rather answer five questions that change a decision than close fifty that don’t, and you can tell which is which before you start. You’d rather teach ten people to pull the number themselves than be the one they depend on, and you get something out of watching someone find their own answer. You go looking for the question instead of waiting to be handed one. You say the uncomfortable thing early, when it’s still cheap, and you’re happy to be wrong in public if it gets us to the right answer faster.

You probably won’t if

You want to work from the data and not from the people. Half of this job is in conversation with teams who don’t think in tables, and if that reads as an interruption to the real work, it isn’t the role. Or you need a groomed backlog and precedent to work from: most of this is still unwritten, and you’ll be the one writing it. Or you’d rather go deep in one domain than move across product, hardware, marketing, support and finance.

Office Location

We have an office in Kempele, which you’re welcome to use, though the role isn’t tied to it.

How we hire

A first conversation with the Head of Data, followed by a short online psychometric and personality assessment you can complete from home in your own time. After that comes an interview with the team, and a working session where we hand you a real modelling problem and spend an hour on it together. You’ll get the material shortly beforehand, not days in advance, and you’re encouraged to use Claude, Cursor or whatever you normally work with. No take-home assignment. We’d rather see how you think than ask you about it.