Job Description

Key Responsibilities

- Design and develop automated ELT processes ensuring a high level of data quality and reliability, using tools such as Spark, Apache Airflow, DBT, Python, Databricks, etc.

- Build and maintain batch and real-time data processing using Apache Kafka and AWS services like SNS, SQS, and Kinesis, supporting microservice architectures.

- Maintain the data lake in AWS and Databricks, building well-governed, query-oriented data models for efficient consumption.

- Implement monitoring and observability for the pipelines you work on.

- Collaborate cross-functionally, assisting other teams in identifying, collecting, and processing valuable information for diverse business use cases.

- Support the Data Science team by helping ensure stable, scalable environments for model deployment and experimentation.

- Build and maintain CICD pipelines, following best practices for automatic deployment and consistency.

- Help administer and maintain deployed infrastructure.

- Follow and contribute to engineering standards and best practices across the Data Engineering team.

- Deliver work on time, managing your own priorities in line with team commitments.

Qualifications

Skills and Experience Required

- 3+ years in data engineering or related data processing roles.

- Strong experience working with varied data formats and sources (JSON, CSV, Parquet, APIs,



multiple DB engines).

- Hands-on experience with data modelling in Big Data platforms (e.G. Databricks, Glue, Snowflake).

- Familiarity with event-driven architectures and AWS.

- At least 2 years of hands-on experience with AWS, especially:

- Core: IAM, S3, EC2, VPC

- Data Services: EMR, Lambda, Batch, SNS, SQS, DynamoDB, Glue, Athena

- Ability to set up and maintain monitoring, alerting, and debugging solutions for your pipelines.

- Experience collaborating with Data Science teams to support production-grade pipelines.

Knowledge

- Solid understanding of computing systems (OS, memory, networks, etc.).

- Proficient in data analysis and interpretation, with a good grasp of statistics and data quality methods.

- Strong SQL and performance tuning skills.

- Good knowledge of Big Data technologies, Spark, and object storage systems.

- Proficiency in Python (and optionally Scala), with strong coding and testing practices.

- Familiarity with Docker;
Kubernetes experience is a plus.

- Airflow (or similar) for orchestration.

- Good understanding of IaaC and CICD principles.

- Dashboard and storytelling experience for data visualization.

Other

- Strong communication skills.

- Proactive mindset and collaborative team spirit.

- Comfortable working within an agile team setup.

📌 Data Engineer (Madrid)
🏢 Ryanair Group Holdings
📍 Madrid