Data (Analytics) Engineer

Floryn; 's-Hertogenbosch; Hybrid; EUR 45,000 - 67,000; Full time

Benefits offered by this job

Vacation days 30

8% holiday pay

Pension contribution

€2,500 personal development budget

MacBook and gear

Hybrid working

NS Business Card

Electric lease bike or €0.23/km

Autonomy to drive data improvements

International scale-up environment

Job summary

Floryn is hiring a Data (Analytics) Engineer to build the scalable data foundation and enable fast, reliable decision making. You’ll write code, model data and automate quality checks, delivering production-ready solutions with impact across analytics, product and business teams.

You’ll transform data models in dbt, define metrics, and automate checks while collaborating with Analysts, Data Scientists and Developers.

Qualifications

• At least two years of experience in data, analytics or engineering close to data.
• Strong SQL skills and cloud data warehouse experience (BigQuery/Snowflake/Redshift).
• Able to solve technical problems via code and data modeling.
• Experience with version control, testing and documentation practices.
• Ability to translate business questions into metrics or tech solutions.
• Clear communication with technical and non-technical colleagues; English or Dutch.

Responsibilities

• Architect, optimise and transform data models in dbt for reliable data.
• Build semantic layer so metrics are defined once and reused.
• Automate checks and root-cause analysis around key metrics.
• Write SQL and use Python for prototyping, automation and analysis.
• Maintain data and code quality with tests and CI/CD.
• Collaborate with Analysts, Data Scientists, Developers and Commercial teams.
• Prototype ideas with stakeholders and iterate improvements.
• Identify bottlenecks and drive improvements end-to-end.

Skills

SQL proficiency

Data modeling

Version control

Testing & documentation

Metric thinking

English/Dutch comms

Tools

dbt

Metabase

Hightouch

Fivetran

Estuary

Prefect

Python

Job description

As a Data (Analytics) Engineer at Floryn, you'll work at the intersection of engineering and analytics, building the scalable data foundation the business relies on. You'll write code, model data, define metrics and automate quality checks. Your work has a direct impact on how quickly and reliably teams can make decisions, and what you build today can already be in production tomorrow.

Floryn is a fast-growing Dutch fintech making business financing faster, more transparent and more flexible through its own technology. Since 2016, Floryn has provided over €800 million in financing to SMEs and now employs more than 80 Dutch and international colleagues.

The challenge

Floryn isn't a dashboard factory. Someone asks for a new metric or report? Before you start building, you'll first want to know what they're actually trying to understand or decide. And that critical thinking doesn't stop once you've built it. If an analysis returns 43.2%, you don't just check whether your query runs correctly; you also ask whether 43.2% makes sense in the first place.

Same goes for technical improvements: a process that is unnecessarily manual or a codebase that could be more reliable? Dig into it, propose a better approach and build it. At Floryn, you'll have the freedom to investigate the problem and take a solution from idea to implementation.

What will you be doing?

• Architect, optimise and transform data models in dbt, enabling analysts and the business to work quickly with reliable data.
• Build out the semantic layer so that important metrics are defined once and used consistently across the organisation.
• Automate checks and root-cause analysis around key metrics, making it easier to see what's changing and why.
• Write plenty of SQL and use Python for prototyping, automation and more complex analysis.
• Keep both data and code trustworthy through version control, testing, documentation and continuous integration.
• Work with Analysts, Data Scientists, Developers and Commercial teams to translate business questions into the right metric, dataset or technical solution.
• Build prototypes to test new ideas quickly and improve them together with stakeholders.
• Identify technical bottlenecks yourself and take improvements from initial idea through to implementation.

Technology

Floryn works with data across customer behaviour, marketing, sales, CRM, loan administration, company financials and more.

dbt is used for transformations and data modelling, Metabase for BI, Hightouch for reverse ETL and Fivetran and Estuary for data ingestion. Prefect runs recurring jobs, while Python is used for prototyping and heavier analysis.

You don't need to have worked with every tool in the stack already. dbt experience, for example, isn't a hard requirement. What matters more is that you understand how to build and test reliable, scalable and reusable data models.

What does Floryn offer?

• A salary between €4,000 and €6,000 gross per month based on 40 hours, depending on your experience.
• 30 vacation days, 8% holiday pay and a pension contribution.
• A €2,500 personal development budget for books, courses, training and conferences, plus a well-being allowance.
• A MacBook, noise-cancelling headset and any other high-end gear you need to do your best work.
• Hybrid working, with regular collaboration at the office in the centre of 's-Hertogenbosch.
• A 1st class NS Business Card, an electric lease bike or €0.23/km when travelling with your own transport.
• Plenty of autonomy to identify technical improvements, initiate projects and help shape Floryn's data environment.
• An international scale-up environment with direct communication, sane working hours, company outings, ski trips and offsites.

What you bring

• At least two years of relevant experience in data, analytics or engineering in a role working close to the data.
• Strong SQL skills and experience with a cloud data warehouse such as BigQuery, Snowflake, Redshift or similar.
• Confidence in solving technical problems through code.
• Experience building reliable and reusable data models and working with version control, testing and documentation; dbt experience is a plus, not a requirement.
• The ability to turn a vague business question into the right metric or technical solution, rather than simply building what was requested.
• Clear communication with both technical and non-technical colleagues, good command of Dutch or English and willingness to work regularly from the 's-Hertogenbosch office.

Where will you work?

You'll join Data Operations: a compact team covering different technical and analytical specialisms. You'll work closely with an Analytics Engineer and Senior Data Analyst, while regularly collaborating with Data Science, Development and teams such as Marketing, Risk and Sales.

As a Data (Analytics) Engineer, you'll be a technical sparring partner within the team. You'll review each other's work, challenge decisions around models and code and help keep the technical bar high.

The way of working is autonomous and focused. There's a short daily stand-up and a weekly session to discuss progress and new projects. Outside of those moments, you'll have long blocks of time to actually build. Missing context? You're expected to step away from your screen, ask questions and find the people who can help.

What does your day look like?

You and the analyst work out the problem together.

An analyst comes to you with a request for a new metric. The two of you sit down and figure out which business decision it actually supports. That conversation often lands somewhere neither of you started, and the model you end up building is a better answer than the one that was asked for. Then you build it.

You build, then you check whether the numbers are true.

dbt and SQL for most of it, Python where it earns its place, and Claude Code doing a real share of the typing. A green build is not a correct answer. You check the grain, the joins and the edge cases, and you write the tests that catch it next time.

Working with agents raises the bar instead of lowering it.

Claude Code is part of how everyone here builds, every day. Someone needs a change to a Metabase dashboard, and with our own Metabase skill that is a few minutes of your attention rather than an afternoon. The speed only holds if the foundation underneath is solid, so review, testing and unambiguous model definitions matter more here, not less. It also shapes what you build. We are exposing our metric definitions to agents so the business can ask its own questions, which means your models are what those agents reason over. Vague semantics survive in a dashboard. They do not survive an agent.

You review, and you get reviewed.

A colleague questions your incremental strategy or the grain of your model, and you do the same for their work. Prototypes go in front of people early, while they are still ugly. Feedback on a rough version beats a polished answer to the wrong question.

You fix things nobody asked you to fix.

Builds get slower. A Prefect flow falls over on late-arriving data. The same metric turns out to be defined in three places, with three answers. And sometimes a backend developer changes a column type without a backfill, and your model quietly starts producing nonsense that nobody notices for a week. Spotting that, tracing it back and making it impossible next time is the work. If your plan holds up, it becomes your next project in the weekly planning.

Then you close your laptop. We build for the long term here, and that does not require your evenings.