Senior Data Engineer (Stockholm/Remote)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 20h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 36d ago
The date the source published, not the day we noticed it (2026-08-10). Last seen at its source just now.
Is it remote?
The listing says yes
The location field doesn't say remote, so our assessment is based on the title or the description. Read the listing before applying.
Who may apply?
Sweden
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay €70.6k–104k/yr
Middle 50% of 40 listings that do state pay — Engineering · Senior · EMEA · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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greenhouse employer's own board first seen 6d ago · last seen just now
The listing
Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
You will be:
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Owning and operating existing data and feature pipelines powering recommendation and advertising machine learning systems.
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Ensuring billions of daily events flow reliably through ingestion and processing, supported by monitoring, data-quality checks, and dependable backfills.
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Supporting Data Scientists and Analysts by providing reliable data for production models and experiments.
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Taking over in-flight initiatives and maintaining momentum on ongoing pipeline and platform improvements.
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Troubleshooting and resolving data issues to keep the platform stable and within SLA.
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Maintaining documentation and runbooks to ensure smooth knowledge transfer.
Your profile:
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Several years of hands-on Data Engineering experience, with the ability to become productive from day one and work independently.
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Strong programming skills in Python, SQL, and Spark, with proven experience building and operating large-scale ETL/ELT pipelines.
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Hands-on experience with an orchestration tool such as Airflow.
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Experience working with a major cloud provider, preferably GCP; AWS or Azure experience is also welcome.
- Hands on experience with Kubernetes.
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Solid software engineering fundamentals, including Git, CI/CD, testing, and clean, maintainable code.
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Strong ownership mindset, reliability, and the ability to troubleshoot production issues independently.
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Clear communication skills and the ability to collaborate effectively with Data Scientists, Analysts, and Engineering teams.
- Working in hybrid (Stockholm) or fully remote model.
Nice to have:
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Experience with a feature store or production ML data pipelines.
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Hands-on experience with BigQuery.
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Exposure to recommendation systems, ranking, or advertising data.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision