Data Engineer - Basel

ekona; Basel; Hybride; CHF 110 000 - 150 000; Plein temps

Résumé du poste

ekona is looking for data engineers to own data foundations end to end. You will build ELT pipelines with dbt and Airflow on Snowflake and other cloud platforms, turning messy master data into trusted models used by planning tools and AI agents.

You’ll profile, validate and reconcile data across ERP and PLM sources, implement data quality checks, and collaborate with enterprise data teams and domain experts to keep data accurate and ready for decision making.

Qualifications

• Several years of professional data engineering experience
• Experience building production ELT pipelines
• Strong SQL and Python skills
• Hands-on with dbt, Airflow and Snowflake or equivalents
• Solid data modeling and good engineering practices

Responsabilités

• Build pipelines that hold up using ELT with dbt/Airflow on Snowflake
• Profile, reconcile and ensure data quality across sources
• Work with ERP/PLM data like SAP and master data
• Support applications and AI with reliable data
• Ship code with version control, CI/CD and IaC
• Help shape data engineering playbook and tooling

Connaissances

SQL

Python

dbt

Airflow

Snowflake

Data modeling

Cloud platforms

Git workflows

CI/CD

Containers

Data quality

Stakeholder comms

English

German

Outils

Dagster

Prefect

Databricks

BigQuery

Description du poste

For engineers who make data trustworthy, and make it move.

At ekona, we build production-grade AI systems for clients in multiple industries including life sciences, supply chain, consumer goods and finance. Every planning tool, forecasting engine and AI agent we ship depends on data that has to be right. That data starts as messy master data, ERP extracts, product hierarchies and documents. We turn it into models people trust and decisions they can defend.

We're looking for data engineers who own that foundation end to end. You don't wait for a perfect spec of the source systems. You dig into the data, find whats broken, fix it, and make sure it stays fixed. And you can sit with a clients data team and their domain experts and speak both languages.

If that sounds like you, keep reading.

What You\'ll Do

Build pipelines that hold up. Design and run ELT pipelines with dbt and Airflow on Snowflake and other cloud data platforms. Model data into clean, documented, tested marts that applications and AI agents can rely on.

Make data trustworthy. Profile and reconcile data across sources and environments. Build data-quality checks into the pipeline, so problems surface before a planner or an agent sees them.

Work with real enterprise sources. ERP and PLM systems such as SAP, master data, product hierarchies and bills of materials, plus the spreadsheets and documents nobody planned for.

Feed applications and AI. Work alongside our AI and software engineers so data lands where it is needed: planning and simulation tools, forecasting models, knowledge graphs and retrieval systems.

Ship like a software engineer. Version control, code review, CI/CD, infrastructure as code, and changes promoted properly from dev to production.

Shape how we work. Help build our data engineering playbook, templates and tooling. Your ideas will be heard.

What We\'re Looking For

How you think about data matters more than any single tool. You\'ll likely be a fit if you have:

• Several years of professional data engineering experience, with pipelines running in production
• Strong SQL and Python
• Hands-on experience with dbt, Airflow and Snowflake, or close equivalents such as Dagster, Prefect, Databricks or BigQuery
• Solid data modeling, and the judgment to keep models simple
• Good engineering habits: at least one major cloud platform (GCP, Azure or AWS), Git workflows, CI/CD and containers. Terraform is a plus
• A data-quality mindset: testing, reconciliation and lineage are part of the job, not an afterthought
• The ability to talk to non-technical stakeholders and turn their questions into data that answers them
• Very good English, spoken and written. German is a plus

Nice to have: SAP data (S/4HANA, material master, BOM, PLM); Databricks; graph databases such as Neo4j; data for ML and LLM applications (feature pipelines, embeddings, RAG); experience in consumer goods, life sciences, supply chain or finance; regulated environments.

Why ekona

• Your data powers things people use. Planning tools, forecasting engines and AI agents in production, not reports nobody opens.
• Engineering quality actually matters. Our clients feel the difference, and so will you.
• Real projects, regulated industries. The work has weight.
• Small team, high bar. Autonomy, trust and direct influence on where we go next.

Is This You?

If you would rather receive a perfectly specified ticket and never meet the people who use the data, this probably isn\'t the right place.

If you like getting to the bottom of messy data and building pipelines that make it trustworthy, it probably is.

Why ekona

• Your data powers things people use. Planning tools, forecasting engines and AI agents in production, not reports nobody opens.
• Engineering quality actually matters. Our clients feel the difference, and so will you.
• Real projects, regulated industries. The work has weight.
• Small team, high bar. Autonomy, trust and direct influence on where we go next.

Is This You?

If you would rather receive a perfectly specified ticket and never meet the people who use the data, this probably isn\'t the right place.

If you like going to the bottom of messy data and building pipelines that make it trustworthy, it probably is.

Video Introduction

Optional, but it's the best way for us to get to know you.

A 2-3 minute video. This is your chance to be real with us.

A few things that tell us a lot:

• A problem you chased down because you couldn't let it go. What kept you at it, what you tried, what you built.
• Something you're obsessed with right now. Doesn't have to be work. Just something that's alive for you.
• Anything else that shows us who you are at your best.

We\'re looking for people who figure things out, stay curious, and build things that actually work. Show us that person.

Video Introduction URL (optional)

Share a link to your 2-3 minute video introduction (YouTube, Vimeo, or other platforms).