Multiverse Computing
Multiverse is a well-funded, fast-growing deep-tech company founded in 2019. We are the largest quantum software company in the EU and have been recognized by CB Insights (2023 and 2025) as one of the 100 most promising AI companies in the world.
With 180+ employees and growing, our team is fully multicultural and international. We deliver hyper-efficient software for companies seeking a competitive edge through quantum computing and artificial intelligence.
Our flagship products, CompactifAI and Singularity, address critical needs across various industries:
- CompactifAI is a groundbreaking compression tool for foundational AI models based on Tensor Networks. It enables the compression of large AI systems—such as language models—to make them significantly more efficient and portable.
- Singularity is a quantum- and quantum-inspired optimization platform used by blue-chip companies to solve complex problems in finance, energy, manufacturing, and beyond. It integrates seamlessly with existing systems and delivers immediate performance gains on classical and quantum hardware.
You’ll be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.
We’re committed to building a truly inclusive culture—come and join us.
Responsibilities
· Own the end-to-end design and delivery of data platform architectures — lakehouse, data catalog, and governance — from initial scoping through production release
· Design, implement, and operate large-scale ETL/ELT pipelines and workflow orchestration to ensure data is clean, accurate, versioned, and accessible
· Define data modeling, partitioning, schema evolution, and versioning conventions so datasets remain queryable, interoperable, and reproducible at scale
· Establish and maintain authoritative data catalogs, including schemas, metadata, lineage, sensitivity labels, and access policies
· Validate released datasets against their sources for completeness, correctness, schema consistency, and query performance, defining objective acceptance criteria
· Work closely with Machine Learning and AI Engineers to make data products directly consumable by analytics, APIs, and AI/agent workflows
· Collaborate with clients and cross-functional teams to scope requirements, lead technical sessions, and document architectures for knowledge transfer and internal ownership
· Mentor and support other data engineers, reviewing designs and code and raising the team's engineering standards
· Stay up to date with emerging trends in data engineering — open table formats, data catalogs, orchestration — and drive their adoption where they add value
Required qualifications:
· Bachelors or master's degree in computer science, software engineering, or a related field
· 5+ years of professional experience in data engineering, including ownership of production data platforms or pipelines
· Expert programming skills in Python and strong command of SQL
· Expertise in data modeling, ETL development, and database management, with both SQL and NoSQL databases
· Hands-on experience with lakehouse architectures and columnar / open table formats (e.g., Parquet, Apache Iceberg, Delta Lake)
· Experience with distributed data processing frameworks such as Spark, and with workflow orchestrators such as Airflow or Argo Workflows
· Strong experience with cloud data platforms (Azure, AWS, or GCP), including object storage, containers, and Kubernetes
· Solid grounding in data governance: catalogs, metadata, lineage, access control, and dataset versioning
· Comfortable with Git-based workflows, CI/CD, and infrastructure-as-code working models
· Excellent problem-solving, communication, and collaboration skills; able to lead technical discussions with clients and stakeholders in English
Nice to have:
· Experience with scientific or geospatial data formats and tooling (e.g., Zarr, NetCDF, GRIB2, xarray, H3 spatial indexing)
· Experience preparing and serving data for LLM, RAG, or agent-based applications
· Previous experience in consulting or client-facing delivery teams
Perks & Benefits
· Indefinite contract.
· Equal pay guaranteed.
· Variable performance bonus.
· Signing bonus.
· We offer work visa sponsorship (If applicable).
· Relocation package (if applicable).
· Private health insurance.
· Flexible remuneration: hospitality and public transportation.
· Eligibility for educational budget according to internal policy.
· Hybrid opportunity.
· Flexible working hours.
· Language classes and discounted lunch options
· Working in a high paced environment, working on cutting edge technologies.
· Career plan. Opportunity to learn and teach.
· Progressive Company. Happy people culture
As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The structural expansion of the deep-tech sector from theoretical quantum computing models into commercialized algorithmic layers necessitates an engineering pivot focused on production-grade infrastructure. Senior Data Engineers within this space occupy a critical stabilization point, managing the ingestion, versioning, and validation protocols required to feed quantum-inspired optimization engines and tensor-network frameworks. As organizations transition through modern technology readiness levels, the structural liability shifts from basic algorithmic discovery to industrial-grade data reproducibility and system scalability. Verifiable market signals from national technology strategies and industry consortia emphasize that enterprise readiness depends heavily on resolving the computational translation gap between raw multi-cloud data inputs and specialized hardware execution layers. Consequently, this engineering function serves as the primary mechanism for mitigating long-term execution risks, ensuring that high-compute applications maintain absolute data integrity and cross-platform interoperability across enterprise value chains.
The quantum software and advanced optimization landscape is undergoing a decisive shift from laboratory-scale verification to the integration of high-fidelity data kernels within enterprise ecosystems. While hardware modalities continue to mature independently, the immediate commercial bottleneck resides in data pipeline stability and the optimization of input datasets for complex mathematical models. Current industry focus lies on bridging classical and quantum capabilities at scale, requiring architecture that can support intense throughput without incurring systemic performance friction.
Ecosystem reports from global advisory institutions highlight that the scale of deep-tech data processing introduces severe infrastructure dependencies. The lack of standard data modeling schemas across disparate quantum cloud interfaces forces contemporary organizations to build highly customized, sovereign storage architectures. This operational challenge is compounded by macro talent shortages at the precise intersection of classical big data systems engineering and advanced mathematical processing layers.
Furthermore, integrating advanced optimization frameworks into existing high-performance computing systems introduces strict constraints around data governance, auditability, and pipeline lineage. As commercial entities deploy automated model compression tools and optimization platforms within heavily regulated sectors like finance and energy, the structural reproducibility of datasets becomes paramount. Managing these technical friction points dictates whether an enterprise can successfully move its compute workloads from experimental pilot phases into real-world industrial environments.
The capability architecture for this role type centers on the absolute synchronization of modern open table formats and distributed data processing frameworks with cloud-native containerized platforms. Maintaining high-throughput pipelines requires rigorous configuration of schema evolution protocols, data partitioning strategies, and automated orchestrators capable of managing elastic hybrid workloads. These systems engineering disciplines are vital for ensuring that downstream artificial intelligence agents and optimization models consume verified, low-latency data inputs.
Furthermore, structural enablement relies on the implementation of advanced dataset serialization, cataloging, and precise metadata tracking layers across multi-cloud environments. The integration points between distributed classical databases and the specialized APIs feeding deep-tech software stacks represent high-leverage dependencies where minor data mutations can invalidate entire downstream processing cycles. Establishing robust data validation scripts, complete lineage graphs, and secure governance matrices directly underpins the operational throughput of the entire systems engineering value chain. - Accelerates the transition of advanced optimization platforms from experimental compute workloads to deterministic enterprise application deployment
- Mitigates data ingestion risks by implementing standardized schema evolution protocols across highly fragmented multi-cloud operational infrastructures
- Facilitates seamless interoperability between legacy enterprise resource planning frameworks and high-performance quantum-inspired software layers
- Optimizes computational throughput via high-leverage data partitioning strategies designed for tensor network model requirements
- Strengthens organizational data governance models through the systematic implementation of automated data catalogs and access controls
- Reduces downstream pipeline friction for machine learning and artificial intelligence engineering functions utilizing compressed foundation models
- Minimizes system downtime during hybrid classical-quantum data processing sequences through the deployment of fault-tolerant orchestration architectures
- Enhances the structural reproducibility of large-scale corporate data products via rigorous dataset versioning and lineage tracking
- Protects capital allocations in deep-tech software development by ensuring the integrity of complex industrial input sets
- Supports regional technology sovereignty objectives by establishing audit-ready data processing workflows that meet strict compliance demands
- Standardizes verification methodologies for enterprise analytics layers interacting with high-dimensional scientific and financial datasets
- Streamlines technical knowledge transfer between client-facing delivery consulting units and internal core engineering infrastructure groupsIndustry Tags: Deep Tech Infrastructure, Lakehouse Architecture, Data Pipeline Engineering, Distributed Computing, Multi-Cloud Governance, Tensor Network Enablement, Algorithmic Reproducibility, Systems Interoperability, Advanced Optimization Pipelines
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