Experteer Overview In this role you will design and build automated ELT pipelines to ensure data quality and reliability, leveraging Spark, Airflow, DBT, Python, and Databricks. You will develop batch and real-time data processing using Kafka and AWS services to support microservice architectures, while maintaining a governed data lake in AWS and Databricks. The role involves monitoring, observability, and collaboration with cross-functional teams to unlock valuable business insights and support model deployment. You will contribute to CI/CD, infrastructure maintenance, and engineering best practices, enabling scalable, production-grade data workflows. This position offers an opportunity to shape data-driven,跨Compensaciones / Incentivos
- Design and develop automated ELT processes with a focus on data quality and reliability
- Build and maintain batch and real-time data processing using Kafka and AWS services (SNS, SQS, Kinesis)
- Maintain governed data lake in AWS and Databricks; develop query-oriented data models
- Implement monitoring and observability for pipelines
- Collaborate with cross-functional teams to collect and process business data
- Support Data Science by ensuring stable, scalable environments for models




- Build and maintain CI/CD pipelines and promote deployment best practices
- Administer and maintain deployed infrastructure
- Contribute to engineering standards and best practices in Data Engineering
- Deliver work on time and manage priorities within the teamResponsabilidades
- 3+ years in data engineering or related roles
- Experience with JSON, CSV, Parquet, APIs, and multiple DB engines
- Hands-on data modeling on Big Data platforms (Databricks, Glue, Snowflake)
- Familiarity with event-driven architectures and AWS
- At least 2 years with AWS core services (IAM, S3, EC2, VPC) and data services (EMR, Lambda, Batch, SNS, SQS, DynamoDB, Glue, Athena)
- Ability to set up monitoring, alerting, and debugging for pipelines
- Experience collaborating with Data Science teams on production-grade pipelines
- Strong understanding of computing systems; solid data analysis andstatistics knowledge
- Strong SQL and performance tuning skills
- Proficiency in Python (and optionally Scala); knowledge of Docker; Kubernetes is a plus
- Experience with Airflow or similar orchestration; understanding of IaaC and CI/CD; data visualization skillsRequisitos principales
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📌 Data Engineer (Madrid)
🏢 Ryanair
📍 Madrid