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# Data Engineer

- **Company:** Biorce
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Clinical Data Repository, Cloud Storage, Data Validation, Information Engineering, Data Fusion, Data Governance, Extract Transform Load (ETL), Data Normalization, Data Warehousing, DevOps, Fault Tolerance, Data Flow Control, Python (Programming Language), Machine Learning, Meta-Data Management, Metadata Repositories, DataOps, SQL Databases, Data Streaming, Google Cloud, Data Storage Technologies, Data Ingestion, Apache Spark, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Api Design, Terraform, Looker Analytics, Data Pipelines, Apache Beam, Docker
- **Published:** August 15, 2026
- **Apply:** https://es.trabajo.org/oferta-9000-eb797218e4f1c87ec7aca15fc5ae653f

## About the Role

Build and orchestrate complex data ingestion workflows from diverse clinical, research, and third-party sources. - Collaborate with data scientists to enable seamless model training, feature generation, and inference data flows. - Ensure data quality, integrity, and lineage across all systems through rigorous validation and monitoring. - Develop and optimize SQL and Python-based transformations to ensure high performance and maintainability. - Manage data storage, partitioning, and lifecycle strategies for efficiency and cost control. - Ensure compliance with SOC2, ISO 27001, HIPAA, GDPR, and clinical data governance standards in all data operations. - Continuously improve internal frameworks for ingestion, metadata management, and data documentation. - Contribute to cross-functional discussions to shape the evolution of Biorce's data and AI architecture. Requirements Must-haves - 3+ years of professional experience in Data Engineering or related roles. - Proven
hands-on experience with GCP data tools: BigQuery, Cloud Storage, Pub/Sub, Data Fusion, Dataflow, Composer, and Cloud Functions. - Strong proficiency in SQL and Python for data transformation and automation. - Experience designing batch and streaming data pipelines with scalable and fault-tolerant architectures. - Familiarity with data modeling, schema design, and data warehouse optimization. - Understanding of API-based ingestion, data normalization, and pipeline monitoring. - Exposure to version-controlled, modular pipeline development, e.g., Terraform, GitOps. - Experience working collaboratively with data scientists and MLOps teams. - Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field. Nice-to-Haves - Experience with clinical, biomedical, or healthcare datasets. - Familiarity with Vertex AI, AI Platform Pipelines, or ML metadata tracking. - Understanding of data governance and cataloging, Data Catalog,
Looker, or similar. - Knowledge of Apache Beam, Spark, or dbt for complex transformations. - Exposure to infrastructure-as-code, Terraform, and containerized workflows, Kubernetes, Docker. - Experience implementing data validation frameworks, e.g., Great Expectations, TFX Data Validation. - Strong focus on reliability, observability, and continuous improvement of

## Description

play a critical role in driving the development of scalable, reliable, and efficient data pipelines in the Google Cloud Platform (GCP) ecosystem. This is an exciting opportunity to build and optimize the data backbone of Biorce's next-generation platform, using modern GCP-native tools such as Data Fusion, BigQuery, and Cloud Storage, in a high-impact, fast-iterating environment. Who We're Looking For We are looking for a skilled Data Engineer to join our growing AI and data team. Someone who can work closely with data scientists, AI engineers, and DevOps to design and operationalize robust data flows that fuel advanced analytics, machine learning, and regulatory-grade insights. This person should be able to contribute to cross-functional discussions, improve internal data frameworks, and shape the evolution of Biorce's data and AI architecture. Key Responsibilities - Design, develop, and maintain scalable ETL/ELT pipelines using Google Cloud Data Fusion, Dataflow, Pub/Sub, and BigQuery.


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