Senior Data Engineer

Sabia Personal; España; Híbrido; EUR 75.000 - 110.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Indefinite contract

Hybrid work in San Sebastian offices

Private health insurance

Educational budget support

Descripción de la vacante

Sabia Personal is seeking a Senior Data Engineer to own end-to-end data platform architectures, including lakehouse, catalogs, and governance. You will design and operate large-scale ETL/ELT pipelines and collaborate with ML/AI teams to make data products consumable by analytics and APIs.

You will partner with clients to scope requirements, mentor engineers, and drive best practices while staying current with open table formats, orchestration, and data governance trends.

Formación

• Bachelor's or master's degree in computer science, software engineering, or related field
• 4+ years of professional data engineering experience with production data platforms or pipelines
• Expert in Python and SQL
• Data modeling, ETL development, and database management (SQL and NoSQL)
• Experience with lakehouse architectures and columnar formats (Parquet, Iceberg, Delta Lake)
• Experience with Spark and workflow orchestrators (Airflow or Argo)
• Strong cloud experience (Azure, AWS, GCP) including object storage, containers, and Kubernetes
• Solid data governance knowledge: catalogs, metadata, lineage, access control, dataset versioning
• Git-based workflows, CI/CD, and IaC practices
• Excellent problem-solving and communication; ability to lead client discussions in English

Responsabilidades

• Own end-to-end data platform architectures (lakehouse, catalogs, governance) from scoping to production release
• Design, implement, and operate large-scale ETL/ELT pipelines and workflows
• Define data modeling, partitioning, schema evolution, and versioning conventions
• Establish and maintain data catalogs, schemas, metadata, lineage, and access policies
• Validate datasets against sources for completeness and performance with objective criteria
• Collaborate with ML/AI engineers to make data products consumable by analytics and APIs
• Work with clients to scope requirements, lead technical sessions, and document architectures
• Mentor data engineers, review designs and code, raise engineering standards
• Keep up with trends in data engineering and adopt where valuable

Conocimientos

Python

SQL

Data modeling

ETL development

Lakehouse

Spark

Airflow

Argo Workflows

Cloud platforms

Git & CI/CD

Educación

Bachelor's or Master's in CS/SE

Herramientas

Parquet

Apache Iceberg

Delta Lake

Spark

Airflow

Argo Workflows

Kubernetes

Azure/AWS/GCP

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. 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 masters 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. 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 teams 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