Senior Storage & Data Engineer

CSCS is looking for a Data & Storage Engineer, working at the intersection of high-performance storage and research data management.

CSCS (operated by ETH Zurich, with offices in Lugano and Zurich) runs supercomputing infrastructure for researchers across academia and industry. This is a two-year position

.

You'll work across two layers: the storage layer — throughput, integrity, and tiering at multi-petabyte scale — and the data layer above it, tackling lineage, provenance, discoverability, and access patterns. The goal is to close the gap between raw bytes sitting on a parallel filesystem and data that researchers can actually trust, find, and reproduc

e.

Job descripti

• on: Bridge ingestion and use. Design the pipelines and metadata that turn ingested data into something findable and consumable — catalogs, schemas, and access layers that match how training jobs and simulations actually read, not just where bytes
• sit.Make data traceable. Build lineage and provenance so any dataset, checkpoint, or result can be traced back to its inputs and transformations. Reproducibility is a first-class requirement here, not a retro
• fit.Tune for the workload. Optimise parallel filesystems (Lustre, GPFS) and object storage for the concurrency, small-file, and large-checkpoint patterns of distributed GPU training and HPC simulat
• ion.Operate at scale, safely. Design and run multi-petabyte storage with the integrity and availability scientific work depends on — erasure coding, redundancy, hot-to-archival tier
• ing.Automate everything. Deploy and scale storage and data services as code. Snowflake infrastructure doesn't survive at this sc
• ale.Make it observable. Instrument storage health, capacity trends, and pipeline performance so problems surface before users feel t
• hem.Translate. Turn real access patterns from domain scientists and ML engineers into technical requirements — and push back when a request would quietly break something downstr

eam.

Pro

• file: A technical degree (CS, engineering) or equivalent experience that demonstrates the same
• depth.Solid storage grounding: filesystems (block and object), performance tuning, redundancy (RAID, erasure co
• ding).Python, and comfort automating infrastructure (Ansible, Terraform, or sim
• ilar).A working understanding of how ML and scientific workloads consume data — billions of small files, large checkpoints, sharding — and why naive layouts fall
• over.A point of view on data lineage, provenance, or reproducibility — and ideally tooling you've used to enfor

ce it.

What helps you sta

• nd out: Hands-on parallel filesystems (Lustre, Spectrum Scale/GPFS) or distributed storage (Ceph
• , VAST).Scientific data formats — HDF5, Zarr, Parquet — and opinions on when each earns it
• s place.Object storage (S3) interfaced with ML frameworks (PyTorch, Tens
• orFlow).Orchestration (Kubernetes, Argo) and data-movement
• tooling.Data versioning / cataloguing (e.g. DVC, lakeFS, a metadata catalog) and familiarity with FAIR data pri
• nciples.CI/CD and provisioning: GitLab CI, HashiCorp Vaul

t, MAAS.

What

• you get: Hardware and scale you won't find in enterprise IT — and problems with no vendor
• playbook.Work that directly enables published science and frontier-scale model
• training.Room to shape how data is managed, not just maintained, in an environment that takes it

seriously.

Curious? Read more and apply now > https://jobs.ethz.ch/job/view/JOPG_ethz_55peH7G

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