Senior Data Engineer

Sabia Personal; Meis; Híbrido; EUR 70.000 - 120.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Indefinite contract

Private health insurance

Visa sponsorship (if applicable)

Relocation package (if applicable)

Hybrid opportunity in San Sebastian

Flexible working hours

Educational budget

Descripción de la vacante

Sabia Personal is seeking a data engineer to design and operate lakehouse data platforms, data catalogs, and governance from conception to production. You will build large-scale ETL/ELT pipelines, define schemas and versioning, and ensure data is clean, versioned, and accessible for analytics and AI workflows.

You will collaborate with ML/AI teams, mentor peers, and drive modern data tooling adoption while communicating with clients in English. A hybrid working model is available in Spain.

Formación

• Bachelors or master's degree in computer science, software engineering, or a related field.
• 4+ years of professional experience in data engineering with ownership of production data platforms or pipelines.
• Strong Python and SQL skills.
• Experience in data modeling, ETL development, and SQL/NoSQL databases.
• Hands-on with lakehouse architectures and Parquet/Delta Lake formats.
• Experience with Spark and orchestration tools like Airflow or Argo.
• Cloud data platforms expertise (Azure, AWS, or GCP) including containers and Kubernetes.
• Data governance fundamentals: catalogs, metadata, lineage, access control, versioning.
• Git-based workflows, CI/CD, and infrastructure-as-code experience.
• Excellent problem-solving and English communication; able to lead client discussions.

Responsabilidades

• Own the end-to-end design and delivery of data platform architectures — lakehouse, data catalog, and governance — from scoping to production release.
• Design, implement, and operate large-scale ETL/ELT pipelines and workflow orchestration to ensure clean, versioned, accessible data.
• Define data modeling, partitioning, schema evolution, and versioning conventions for scalable datasets.
• Establish and maintain authoritative data catalogs with schemas, metadata, lineage, and access policies.
• Validate released datasets against sources for completeness, correctness, and performance.
• Collaborate with ML/AI engineers to make data products consumable by analytics, APIs, and agents.
• Lead technical sessions with clients and cross-functional teams; document architectures for knowledge transfer.
• Mentor and review teammates to raise engineering standards.
• Stay current with trends in data engineering and drive adoption when valuable.

Conocimientos

Python

SQL

Data modeling

ETL/ELT

Lakehouse

Parquet/ Delta Lake

Spark

Airflow

Kubernetes

Cloud platforms

Data governance

Git & CI/CD

English communication

Geospatial data formats

LLM data prep

Educación

Bachelor's or Master's in CS / Software Engineering

Herramientas

None specified

Descripción del empleo

Descripción del trabajo

Our client is a fast-growing deep-tech company founded in 2019 and recognized by CB Insights as one of the 100 most promising AI companies globally. ¿Listo para inscribirse? Antes de hacerlo, asegúrese de leer todos los detalles pertenecientes a este trabajo en la descripción a continuación. They are the largest quantum software company in the EU, with 250+ employees worldwide and growing, delivering advanced solutions trusted by leading global enterprises across several critical industries, including finance, energy, manufacturing, telecom, and industrial sectors.

Required Qualifications:

• Bachelors or master's degree in computer science, software engineering, or a related field
• 4+ 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 and Benefits:

• Indefinite contract.
• Equal pay guaranteed.
• Variable performance bonus.
• Signing bonus.
• They offer work visa sponsorship (If applicable) and relocation package (if applicable).
• Private health insurance.
• Eligibility for educational budget according to internal policy.
• Hybrid opportunity in their offices located in San Sebastian.
• Flexible working hours.
• Language classes and discounted lunch options.
• A high-performance, collaborative environment, operating at pace on cutting-edge technologies.
• Career plan.
• xkdbapo Opportunity to learn and teach.

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