Data Ops Engineer for AI Product

Qonto; Berlin; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading finance workspace company is looking for a Data Ops Engineer for AI products in Berlin. You will build and maintain data pipelines for machine learning systems, ensuring efficient data storage and access patterns. The ideal candidate has experience with Python, data engineering, and SQL. Join a dynamic team committed to simplifying accounting processes for SMEs through innovative solutions. This role offers opportunities for growth and collaboration with talented engineers.

Leistungen

Competitive salary package

Meal vouchers

Public transportation reimbursement

Great health insurance

Employee well-being initiatives

Childcare benefits

Monthly team events

Qualifikationen

• Minimum 3 years of experience with Python and software engineering skills.
• Experience in data engineering, ideally applied to ML use cases.
• Strong knowledge of batch and real-time pipeline design.

Aufgaben

• Build and maintain real-time and batch end-to-end data pipelines for ML.
• Design and optimise data storage and access patterns for scale.
• Deploy and manage model-serving systems for production integration.

Kenntnisse

Python

Data Engineering

SQL

CI/CD

Real-time Pipeline Design

Tools

Kafka

Airflow

DBT

Snowflake

Jobbeschreibung

Our mission

Creating the freedom for SMEs to succeed in business and beyond, by delivering Europe’s leading finance workspace. We combine business‑class tools (seamless invoicing, spend management, and pre‑accounting) with unwaveringly attentive 24/7 support, designed to help businesses breeze through all things finance.

Our journey

Founded by Alexandre and Steve in July 2017, Qonto has rapidly gained trust, serving over 600,000 customers. Thanks to our wonderful team of 1,600+ Qontoers, we also made it to the LinkedIn Top Companies French ranking!

Our values

• Customer focus – Prioritize customers in everything you do
• Ownership – Own your part, get things done
• Teamwork – Make (team)work easy
• Mastery – Continuously raise the bar
• Integrity – Always do what’s right, and respect people

Our beliefs

At Qonto, we're committed to fostering a welcoming environment where everyone can thrive. We prioritize evaluating applicants based solely on skills and potential, ensuring diversity with 55% international team members, 44% women, and 20% parents. Join us in building a workplace that celebrates diversity and individuality.

Discover the steps we took to create a discrimination‑free hiring process.

Data Ops Engineer for AI Product

As a Data Ops Engineer for our AI product, you’ll shape the foundations that power our machine learning systems, helping us build smarter, faster, and more reliable financial tools.

As a Data Ops Engineer for our AI product, you will

• Build and maintain real‑time and batch end‑to‑end data pipelines for ML model training and inference in a production and fast‑paced environment.
• Design and optimise data storage and access patterns for scale and performance.
• Deploy and manage model‑serving systems for seamless production integration.
• Partner with ML Engineers to ensure data quality, scalability, and monitoring.

What you can expect

• You’ll be joining a dynamic team to enable machine learning solutions that simplify accounting processes for SMEs and freelancers across Europe.
• At Qonto, we use lean methodology with a focus on continuous delivery, allowing rapid iteration and feedback on data infrastructure improvements.
• Work in pair with Machine Learning Engineer, and put into production models tailored to customer needs.

What You’ll Work With

• Build–run–own Python services with strong system design, implementation, CI/CD, observability, and continuous improvement.
• SQL to store data, and query data efficiently.
• Stream Processing (Kafka, or similar) to process events in real‑time.
• Orchestration & Data tools (Airflow, DBT, Snowflake).
• Collaborate across ML, backend, and product teams.

About your future manager

Marianne is deeply involved with the development team, providing swift problem‑solving support and encouraging proactive discussions on technical decisions. With a focus on removing blockers and fostering innovation, she empowers team members to take ownership of their projects and grow professionally within the company.

About you

• Minimum 3 years of experience with Python and software engineering skills (microservices, CI/CD, testing).
• Minimum 3 years of experience in data engineering, ideally applied to ML use cases.
• Strong knowledge of batch and real‑time pipeline design.
• Proficiency in SQL and data modeling for performance.
• A team player who bridges technical and product discussions.
• [Bonus] Good understanding of ML workflows and data lifecycle.

We are looking for someone who can be based in one of our offices (Paris, Berlin, Milan, Barcelona, or Belgrade), as this position is not open to fully remote arrangements.

At Qonto we understand that true diversity isn't just about ticking boxes on a hiring checklist. Apply regardless of the boxes you tick! Who knows? You may have the missing piece of the puzzle we've been searching for all along.

Perks

• Offices in Paris, Berlin, Milan, Barcelona, and Belgrade.
• Competitive salary package.
• Meal vouchers.
• Public transportation reimbursement (part or global).
• Great health insurance (depending on the country).
• Employee well‑being initiatives: access to Moka Care to take care of your mental health and great offers for sports and wellness activities.
• A progressive disability and parenthood policy (1 in 6 of Qonto employees is a parent!) and childcare benefits with selected partners.
• Monthly team events.

Our hiring process

• Interviews with your Talent Acquisition Manager and future managers.
• A remote or live exercise to demonstrate your skills and give you a taste of what working at Qonto could be like.

Find more information about our interview process on our careers website.

On average, our process lasts 20 working days and offers usually follow within 48 hours.

To learn more about us:

• Qonto's Blog
• Les Échos
• L'Usine Digitale
• Courrier Cadres

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