Hey you.

If you’re an experienced data professional and occasionally find yourself thinking about what a sensible next step could look like, this might be worth a read.

Ilmiö exists to build better working life for ourselves and the people we work with. We work closely with experienced professionals and Nordic tech companies to help them find the right way to work together.

Over the past years, we’ve seen steady demand for senior data expertise. Not because companies are chasing trends, but because data platforms, pipelines, and foundations have become a critical part of everyday business. Many organisations are looking for experienced people who want to take long-term ownership and develop these setups over time. With demand for senior data expertise remaining strong, you may have more options on the market than you think.

Whether you’re actively job hunting or just keeping an eye on the market, this open application is meant for that moment when you want to understand what kind of roles are actually available and how they compare to where you are now.

A bit about the companies we work with

We work with Nordic and European technology-driven companies in different stages of growth. This includes product companies and SaaS businesses, scale-ups formalising their data foundations, and more established organisations modernising existing data platforms.

What they have in common is a need for experienced data professionals who want to be part of a team and take responsibility over time. These are roles where data work is closely tied to real products, services, and business decisions.

What the day-to-day work often looks like?

People who step into these roles usually work hands-on with data as part of a broader product or platform team. Depending on the role and the company, the work can include:

• building and maintaining data platforms, data warehouses, or lakehouse solutions

• designing, building, and operating data pipelines in cloud environments

• shaping data models to support analytics, reporting, and downstream use cases

• improving existing data setups in terms of structure, reliability, and clarity

• working closely with product teams, developers, analytics, and other stakeholders

People typically come into this kind of work with a fairly consistent core of data engineering and analytics experience. This often includes hands-on work with SQL and data modeling (such as dimensional models or Data Vault), Python, PySpark, or similar data processing tools, cloud environments like Azure, AWS, or GCP, and modern data platforms such as Databricks, Snowflake, Synapse, BigQuery, or Redshift.

These roles exist across both consulting-driven and in-house environments. Job titles vary widely, but the underlying work tends to be more similar than different.

How we work with you?

Our role is not to push roles or rush decisions. Instead, we take the time to:

• talk with you about your background and what kind of role could make sense next

• map that against the companies we work with

• go through role focus, expectations, and context openly before anything moves forward

• keep the process transparent and human

We focus on what you’ve built, maintained, and taken responsibility for, rather than what your role has been called. The goal is to help you make an informed decision, not just a quick one.

All roles we recruit for are permanent positions, and the employment relationship is always directly with our client company. We’ll always discuss any opportunity with you first, and nothing is shared with a client without your consent.

What happens next?

You can leave an open application and tell us a bit about your background and interests. We review applications continuously and reach out when there’s a role or an opportunity that genuinely seems relevant. Even if nothing is a fit right away, the conversation itself can still be useful.

Information about the use of AI in our recruitment process

We use AI to support parts of our recruitment process, such as drafting job descriptions, pre-processing applications, and creating interview notes. AI never makes decisions on its own, all evaluations and hiring decisions are made by humans. Candidate data is processed securely within the EU and is not used to train AI models.