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# Senior Data Engineer

- **Company:** Remotestar
- **Location:** Huelva, Spain
- **Contract:** Permanent contract
- **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Apache HTTP Server, Microsoft Azure, Computer Programming, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Distributed Computing Environment, Interoperability, Python (Programming Language), Machine Learning, Metadata, NoSQL, Operational Databases, Cloud Services, Standard Sql, Software Engineering, SQL Databases, User-Centered Design, Management of Software Versions, Parquet, Apache Spark, Data Lakes, Git Flow, Kubernetes, Information Technology, Data Pipelines
- **Published:** August 7, 2026
- **Apply:** https://www.buscojobs.com.es/senior-data-engineer-en-huelva-ID-366419736

## About the Role

Bachelors or master's degree in computer science, software engineering, or a related field
5+ 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

## Description

Bachelors or master's degree in computer science, software engineering, or a related field5+ years of professional experience in data engineering, including ownership of production data platforms or pipelinesExpert programming skills in Python and strong command of SQLExpertise in data modeling, ETL development, and database management, with both SQL and NoSQL databasesHands-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 WorkflowsStrong experience with cloud data platforms (Azure, AWS, or GCP), including object storage, containers, and KubernetesSolid grounding in data governance: catalogs, metadata, lineage, access control, and dataset versioningComfortable with Git-based workflows, CI/CD, and infrastructure-as-code working modelsExcellent problem-solving, communication, and collaboration skills; able to lead technical discussions with clients and stakeholders in EnglishResponsibilitiesOwn the end-to-end design and delivery of data platform architectures - lakehouse, data catalog, and governance - from initial scoping through production releaseDesign, implement, and operate large-scale ETL/ELT pipelines and workflow orchestration to ensure data is clean, accurate, versioned, and accessibleDefine data modeling, partitioning, schema evolution, and versioning conventions so datasets remain queryable, interoperable, and reproducible at scaleEstablish and maintain authoritative data catalogs, including schemas, metadata, lineage, sensitivity labels, and access policiesValidate released datasets against their sources for completeness, correctness, schema consistency, and query performance, defining objective acceptance criteriaWork closely with Machine Learning and AI Engineers to make data products directly consumable by analytics, APIs, and AI/agent workflowsCollaborate with clients and cross-functional teams to scope requirements, lead technical sessions, and document architectures for knowledge transfer and internal ownershipMentor and support other data engineers, reviewing designs and code and raising the team's engineering standardsStay up to date with emerging trends in data engineering - open table formats, data catalogs, orchestration - and drive their adoption where they add value#J-*****-Ljbffr


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