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# Data Engineer

- **Company:** Inbrain Neuroelectronics
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** OpenTofu, Airflow, Amazon Web Services, Amazon S3, Apache HTTP Server, Systems Engineering, Continuous Integration, Information Engineering, DevOps, Identity and Access Management, Python (Programming Language), Raw Data, SQL Databases, Management of Software Versions, Data Ingestion, Snowflake, Git, Information Technology, Machine Learning Operations, Terraform, GXP
- **Published:** November 14, 2026
- **Apply:** https://www.adzuna.es/details/5965181173

## About the Role

* BSc or MSc in Computer Science, Systems Engineering, Mathematics or a related technical discipline.

Experience and Technical Skills

 * Over 5+ years in Data Engineering, with at least 2 in a senior role designing and owning production pipelines.
 * Strong Python and SQL.
 * Hands-on experience with AWS, especially S3, IAM, Glue and compute services.
 * Experience with a modern Lakehouse or Warehouse stack: Apache Iceberg (or Delta/Hudi) and Snowflake (or a similar warehouse).
 * Experience building ingestion and transformation pipelines with an orchestrator such as Dagster or Airflow.
 * A track record of automated data-quality checks, data versioning and lineage.
 * Comfort with IaC (Terraform/OpenTofu), Git and CI/CD.
 * Fluent English, written and spoken.

Preferred

 * Experience in a regulated environment such as medical devices, pharma, clinical trials or healthcare: GxP, ALCOA+, 21 CFR Part 11, ISO 13485.
 * Experience with clinical or biomedical data, such as EDC/eCRF exports, CDISC (SDTM/ADaM), or de-identified patient data under GDPR/HIPAA.
 * Experience with time-series or signal data, ideally neurophysiology (NWB, BIDS, HDF5), or large sensor and video datasets.
 * Experience with data contracts or schema-as-code.
 * Experience supporting ML teams with reproducible, versioned training datasets.

Behavioural Competencies

 * Ownership: takes a pipeline from design through to operation.
 * Explains data issues clearly to scientists, clinicians and quality teams.
 * Documents work so that it holds up in an audit.

## Description

* Ingestion pipelines: design, build and run pipelines that bring clinical, preclinical and device data (neural recordings, video, sensor and clinical metadata) from study sites into the AWS/Iceberg platform.
 * Data quality: put automated checks in place for completeness, format, required metadata and signal quality (e.g., SNR, impedance), and make failures visible and routed to an owner.
 * Curation and modelling: turn raw data into standardised and curated, analysis-ready datasets in Snowflake (raw * standardised * curated), versioned and documented.
 * Data Contracts: apply Data Contracts at ingestion, working with Clinical and study sites; reject non-conforming data rather than fixing it silently.
 * Traceability and lineage: keep a full path from every dataset back to its source site, study, session and Data Contract version, to meet ALCOA+ and 21 CFR Part 11 requirements.
 * Catalog and access: register datasets in the catalog with owner, source, metadata and access classification, and support role-based access for data users.
 * Enabling ML and analytics: provide reproducible, versioned datasets to the ML Ops and data science teams, including the reference datasets used for model training and validation.
 * Scaling to new data: onboard new studies and data types using reusable patterns, including the high-volume semi-chronic and chronic recordings from 2027-2028.
 * Standards and documentation: define data engineering standards and SOPs and document pipelines for audits and regulatory submissions.
 * Collaboration: work with the Platform Architect, DevOps and MLOps engineers on the platform, and with Clinical, Translational Science and RA&QA on data requirements., To ensure a positive candidate experience, we aim to provide a transparent, efficient, and respectful hiring process, with timely communication and feedback throughout.
 1. People & Culture Screening (30 min) - A brief introduction to learn about your background, career aspirations and interest in INBRAIN.
 2. Hiring Manager Interview (45 min) - A deeper discussion focused on your experience, technical expertise, and alignment with the role and team.
 3. Technical Interview (60 min) - As part of the recruitment process, candidates will be asked to complete a short take-home technical case and present their approach during a technical interview with members of the Data Factory team.
 4. Culture Fit Interview (60 min) - Conversations with key stakeholder and People & Culture Representative.

This approach allows us to get to know you holistically while giving you the opportunity to learn more about our team, culture, and mission.
Why us?