Wait, What Do You Do?

We’re looking for a hands-on Data Engineer to join our Copenhagen office and bring structure to a fragmented data landscape - building a foundation that can be used across the business, by people and by AI. As part of our Revenue Operations team, you will work closely with IT and colleagues across the company to make business data consistent, understandable, and trustworthy. You’ll be joining a newly established function in a growing SaaS company.

The role

This is a hands-on, foundational role: practical execution, not data science. That said, no central data platform exists here yet, so “hands-on” includes real decisions about architecture and tooling - this isn’t a job where you just plug into something that’s already built. Dashboards and reporting matter, but they’re an output. The real goal is data structured and reliable enough to power AI, automation, and better decisions.

This position is part of our Revenue Operations team and reports to Martin Rafn Møller. We’re looking for a candidate with around 3+ years of hands-on experience.

What you’ll do

Platform setup

Own the end-to-end design of our data platform; picking and setting up the stack across ingestion, storage, modeling, and delivery.

Data ingestion

Map the business systems where company data lives (CRM, marketing, project delivery, product, support, and other operational platforms), and bring that data in via managed ELT connectors (e.g. Fivetran, Airbyte, or Weld) or custom-built pipelines, depending on what each source supports.
Work with IT on the custom integrations this requires - API ingestion, database connections, or source-specific flows. As a result of ongoing mergers and acquisitions, expect this to be a regular part of the job, not a rare exception.

Modeling, quality, and documentation

Turn raw source data into a clear, maintainable foundation of cleaned, modeled, reusable datasets, with data models that reflect how the business actually works and clear definitions for entities, relationships, and metrics.
Implement practical data quality checks so missing, duplicated, outdated, or inconsistent data is caught before it affects reporting or downstream automation.
Document sources, transformations, metric definitions, ownership, and access so the foundation is trusted across the business.

Reporting, automation, and AI use cases

Prepare data so it’s genuinely AI-ready, meaning structured, clearly defined, documented, and reliable enough that AI can be applied broadly. Not only for analysis, but for automation, assistants, and AI-driven workflows across the business, as well as for human use.
Replace manual exports and conflicting definitions with automated, repeatable data flows - and build dashboards and reports on top of trusted models where they’re needed.

The company

For over 20 years, Omnidocs has built software for document generation. Born as a consultancy and turned into a SaaS business, the company now helps customers worldwide get the best results from their documents and presentations.

Today, the Omnidocs Group has a strong portfolio comprised of several products and almost 200 professionals across various office locations such as Denmark, The Netherlands, UK, Switzerland and more. Following a strategy of acquiring new companies and technologies, in the past 3 years we welcomed Dania Software, Office Consult, Xential, Eformity, Presentation Solutions and officeatwork to the group.

As we mature into a more product- and data-oriented company, getting our data foundation right has become essential to how we operate and how we make decisions. That’s where you come in.

Your qualifications

The essential:

Hands-on experience with data from real business systems, not just sample datasets or isolated reports
Strong SQL for querying, transforming, modeling, and validating data
Python or another scripting language for data processing, automation, and operational data tasks
Experience building data pipelines into a central data platform - using managed ELT connectors (such as Fivetran, Airbyte, or Weld) and custom-built integrations depending on the source, with a solid grasp of ETL/ELT concepts and data quality principles
Experience with a modern data warehouse, lakehouse, or cloud data platform
A clear grasp of how to structure raw data into clean, reusable models that support reporting, analysis, automation, and AI - including a basic sense of what that asks of data quality and structure
Ability to translate unclear business questions into practical data structures and outputs, with good judgment about when to use a standard connector, coordinate something custom, or leave a process manual for now
Strong communication skills in English

It would be great if you also have an interest and/or experience in:

The modern data stack: dbt for modeling and documentation, Fivetran/Airbyte/Weld for ingestion, Snowflake/BigQuery/Fabric/Databricks for the platform, Dagster/Airflow for orchestration, Git for version control
Hands-on experience building or deploying AI-powered features themselves (e.g. a RAG pipeline, an AI agent, or semantic search) - beyond just preparing data for them
Building data foundations where data structures, ownership, and reporting logic are still immature
Building dashboards in tools such as Power BI, Tableau, Looker, or Metabase
Access control, privacy, and basic data governance

Although you must be versatile across the data engineering process, we’re not looking for a “ninja” or “unicorn”: we expect you to know your strengths and be interested in working on your weaker points. Be open about your core competencies, and where you’d like to improve. Our needs are diverse, and we can accommodate different professional profiles.

Soft skills

You are structured, curious, and pragmatic - comfortable with ambiguity and with imperfect systems and incomplete data, creating order over time. You work independently, ask sharp questions, and challenge requirements when the underlying data logic is unclear. You care whether data is useful, reliable, and reusable, not just whether a dashboard looks good. You use modern tools, including AI, naturally in your work - not as a gimmick, but to move faster, improve documentation, debug, and explore data. We appreciate proactivity, adaptability, and a strongly cross-functional, collaborative approach.

What we offer

Flexible work hours: 9-to-5 or 11-to-19, it’s your choice
Hybrid work arrangements
Work-life balance, in true Scandinavian style
Optional lunch scheme in partnership with a local French brasserie
Discounts in gym membership and work glasses
Partially subsidized weekly massage therapy in the office
Social activities like wine tasting, seasonal parties and team events
Sport activities like running groups and participation in the DHL Relay CPH
Career development opportunities
An informal work environment in our office in the heart of the historic Christianshavn neighbourhood, surrounded by the vibrant life of cafes, restaurants, and cultural sights

Why is this a great time to join

You won’t have to advocate for the value of data: from senior management down, there’s clear recognition of why this foundation matters
This is the first step in building out our data capability - you’ll be an important part of it, and you’ll help set the standards for how data engineering fits in as we mature into a product- and data-oriented company
You will directly influence not only the company’s data foundation, but also your own role, tooling, and processes
You’ll be the first dedicated data hire - no data team to lean on day-to-day, but IT is a close, responsive partner for integrations and infrastructure, and you’ll work closely with your manager for sparring and ensuring progress.
We’re not asking you to build this without the right tools - what you need to do the job well is part of the conversation once you’re in the role

Application

Please apply via the dedicated application link with your CV and brief written answers to a few short questions in the application form. That’s all we ask - no cover letter or portfolio needed. Keep your answers short and to the point; if we move forward, we’ll explore more in the interview.

As we strive for a thorough and unbiased recruitment process, we also kindly ask you to:

Use the dedicated application link (do NOT send your resume over e-mail)
Do NOT send a picture of yourself
Do NOT share your age or birth date

Recruitment steps:

First interview - initial screening with Martin Rafn Møller starting from the 27th of July
Second interview - a deeper conversation with the team, plus a take-home behavioural assessment (aprox. 30 min) and a follow-up with People & Culture
Final call to discuss additional matters, if needed

Questions?

Feel free to reach out to Martin at mrm@omnidocs.com.

Applications will be reviewed on an ongoing basis. We’ll do our best to get back to you within 3-4 working weeks, but please be aware that this timeframe may be extended.

Processing of personal data

By registering in our HR system, BambooHR, and uploading both application and CV, you accept that Omnidocs stores the registered data about you and the data included in the uploaded documents. All information is used solely for recruitment purposes and will be deleted within 12 months of upload. Please do not include person-sensitive data in the application and CV, e.g. CPR number, information about race, religion, trade union conditions, and health information. You can read about Omnidocs’ guidelines for processing personal data here:

https://omnidocs.com/processing-of-personal-data-in-the-context-of-recruitment/