Senior Data Engineer - Evinova

This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.

Evinova, a healthtech leader, is seeking a passionate and experienced Senior Data Engineer to build and automate our data foundation to enable our products, data science, and agents to deliver category leading capabilities. Join us in leveraging cutting-edge technology, data, and AI to revolutionize life sciences and improve billions of lives globally.

In this pivotal role, you will assist in the design, be a senior implementor, and always finding new ways to automate and optimize robust cloud-based data within the Lakehouse, catalogue, pipelines, and operational frameworks that enable rapid innovation and deliver exceptional system reliability. You will be one of the senior data engineers within our team; expected to be hands on, guide, and mentor the others across other teams.

You will need to share your expertise in cloud data tools, patterns, optimizations, automation, and best practices with the whole of Evinova.

Infrastructure Design & Management:

• AWS Data Services: Strong hands-on experience with Lake Formation, Glue (ETL + Catalogue + Schema Registry), Athena, and at least one of EMR / Redshift Serverless. You understand how these compose, not just how each works in isolation.
• Open Table Formats: Production experience with S3 Tables, Apache Iceberg (preferred), or Delta Lake. You understand partition evolution, schema evolution, time travel, and compaction - and when each matter.
• Streaming: Built production streaming pipelines with Kinesis Data Streams or MSK. Comfortable with exactly once semantics, windowing, late-arriving data, and back pressure.
• Infrastructure as Code: AWS CDK (
  Type Script ) or Cloud Formation. You define infrastructure in code, not in the console. CI/CD for data pipelines is expected, we currently use Git Hub Actions, and some Terraform.
• Data Modelling: Can design dimensional models, event schemas, and slowly changing dimensions. Understand the trade-offs between normalized and denormalized storage for different access patterns.
• Governance and Security: Practical experience implementing column-level security, row-level filtering, or tag-based access control. Understands how data classification drives policy.
• Python or Spark: For ETL logic, feature extraction, and data quality validation. PySpark or Spark Scala for distributed transforms.
• AI & Machine Learning: Exposure to AI tools and frameworks is a plus.
• Mentorship: Mentor and guide junior engineers and even your peers, fostering a culture of learning and collaboration. Help in adoption of the tooling, patterns, and automation best practices.
• Collaboration: Partner with cross-functional teams, including product management and security, to align data foundation strategies with business goals and ensure cohesive development and operational workflows.

Required Experience & Qualifications

• Experience: 7+ years in hands on data engineering, with strong experience in SaaS and multi-tenant data platforms. Proven track record of mentoring and helping other team members in data platform related projects.
• Cloud Expertise: Strong understanding of AWS services, including VPC, IAM, EC2, S3, RDS, Lambda, EKS, AWS WAF, and AWS Cloud Trail.
• Data Products: Strong knowledge of S3, RDS, DynamoDB, Kinesis, Glue, Data Zone, Athena, Red Shift Serverless, and AWS Event Bridge.
• Containerization & Orchestration: Strong proficiency in Docker, Kubernetes, Helm, and associated ecosystem tools.
• CI/CD Proficiency: Expertise in CI/CD tools such as ArgoCD and Git Hub Actions.
• Infrastructure as Code (IaC): Advanced experience with AWS CDK (Type Script preferred) and Cloud Formation.
• Security: Good knowledge of IAM, AWS KMS, encryption standards, AWS WAF, and security compliance frameworks including NIST.
• Monitoring & Alerting: Good experience with Open Telemetry, Prometheus, Grafana, AWS Cloud Watch, and AWS Cloud Trail for monitoring and incident response.
• Data & ETL Pipelines: Extensive knowledge with AWS Glue, AWS Kinesis, and Managed Kafka for real-time and batch data processing.
• Programming & Automation: Strong scripting and automation skills using Type Script and Bash.
• Multi-Account AWS Management: Experience managing multiple AWS accounts with AWS Control Tower.
• Communication &
  
  Collaboration:
  
  Exceptional verbal and written communication skills, with the…