Senior Data Engineer at MULTIVERSE COMPUTING

✨ The Stack take on this MULTIVERSE COMPUTING job

• 🏢 MULTIVERSE COMPUTING has 6 jobs here, 6 with visa sponsorship see all
• 🛂 Visa: Highly Qualified Professional permit. Education: Higher education degree guide
• 💡 No salary shown. Ask MULTIVERSE COMPUTING if it is above the minimum.

Updated 2026-11-05, from our data and official sources.

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About the role

Responsibilities:
- Own end-to-end design and delivery of the data platform: lakehouse architecture, data catalog, governance and production releases.
- Design, implement and operate large-scale ETL/ELT pipelines and orchestrations to ensure clean, versioned and accessible data.
- Define data modeling, partitioning, schema evolution and versioning practices to keep datasets queryable and reproducible.
- Build and maintain authoritative data catalogs with metadata, lineage, sensitivity labels and access policies.
- Validate released datasets against sources (completeness, correctness, performance) and set objective acceptance criteria.
- Partner with ML and AI engineers to deliver data products consumable by analytics, APIs and ML workflows.
- Mentor other data engineers, review designs/code and raise engineering standards across the team.
- Track and evaluate emerging data technologies (open table formats, orchestration, governance) and drive adoption.

Requirements:
- Bachelor’s or Master’s in Computer Science, Software Engineering or related field.
- 4+ years professional experience in data engineering with ownership of production data systems.
- Strong Python and SQL skills; experience building ETL/ELT at scale (Spark, Airflow or similar).
- Hands-on experience with lakehouse/open table formats (Iceberg, Delta) and data cataloging.
- Familiarity with cloud data platforms (AWS/Azure/GCP), Kubernetes, CI/CD and IaC tools.
- Knowledge of data governance, lineage, schema evolution and access controls.
- Experience working with ML teams and delivering data for analytics/AI workflows.
- Strong communication skills and a pragmatic, quality-focused approach to engineering.

Responsibilities

Own the end-to-end design and delivery of data platform architectures, including lakehouses and data governance. Implement large-scale ETL/ELT pipelines and collaborate with AI engineers to create consumable data products.

Requirements

Requires a degree in Computer Science or a related field with over 4 years of professional data engineering experience. Expertise in Python, SQL, distributed processing frameworks, and cloud data platforms is essential.

Key skills

• Python — in 1200 other listings on The Stack
• SQL — in 545 other listings on The Stack
• ETL/ELT — in 20 other listings on The Stack
• Data Modeling — in 184 other listings on The Stack
• Lakehouse Architecture — in 2 other listings on The Stack
• Apache Iceberg — in 3 other listings on The Stack
• Delta Lake — in 22 other listings on The Stack
• Apache Spark — in 14 other listings on The Stack
• Airflow
• Kubernetes
• Cloud Platforms
• Data Governance
• CI/CD
• Infrastructure-as-Code
• Git
• NoSQL

On The Stack (as of 2026-11-05)

• MULTIVERSE COMPUTING has 5 other listings on The Stack, 5 of them marked as offering visa sponsorship.
• 76 visa-sponsored jobs in Spain on The Stack, posted in the last 60 days.
• 47 visa-sponsored data engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about MULTIVERSE COMPUTING

Is this Senior Data Engineer role still open?

It was posted on 2026-07-21. Listings more than 30 days old may no longer be active, so check MULTIVERSE COMPUTING's site before applying.

Does MULTIVERSE COMPUTING offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on MULTIVERSE COMPUTING's site.

Where is this role located?

The listing gives the location as Spain. It is marked on-site.

What skills does this role ask for?

The listing names: Python, SQL, ETL/ELT, Data Modeling, Lakehouse Architecture, Apache Iceberg, Delta Lake, Apache Spark, Airflow, Kubernetes, Cloud Platforms, Data Governance, CI/CD, Infrastructure-as-Code, Git, NoSQL.