Senior Data Engineer

• 23 days of annual holiday
• Company-wide Christmas Break
• Private health insurance
• Flexible compensation benefits through Payflow
• Free Spanish classes
• Udemy learning account
• Annual training allowance to support your professional development
• A high-tech, state-of-the-art office designed for collaboration and innovation
• Flexible and dynamic work environment with the opportunity to grow alongside the company

At INBRAIN, we're not just building technology. We're building a future where outstanding science and innovation can improve the lives of millions of people. If that mission inspires you, we'd love to meet you.

We are scientists, doctors, techies and humanity lovers, with the mission of building neuroelectronic interfaces to cure brain disorders. INBRAIN harnesses the extraordinary material properties of Graphene, the world’s thinnest and nobel-prize winning material, to build safe, uninvasive and highly efficient neural solutions. Our mission is to decode and modulate neural networks to improve people's lives.

As a Data Engineer at INBRAIN, you will be in charge of implementing the internal data collection, ingestion and automatic analysis tools. 

You will be at the forefront of bringing advanced healthcare solutions to market, making a tangible difference in people´s lives worldwide.

• 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.

• 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.

At INBRAIN, we're not just offering a job – we're inviting you to be part of a transformative journey, Are you ready to transform lives? Join us!

#diverseandinclusive 

We believe that a diverse and well-balanced workforce drives innovation. At INBRAIN, we foster the inclusion of all people regardless of culture, age, gender, sexual orientation, identity and diverse abilities or any other status.