Data Engineering Architect - 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 Data Engineering Architect to guide in the structure of the structure our platform-wide conformed data within 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 design, implement, 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-most data architects and engineers within the data foundation team; expected to be hands on, guide, and mentor the team. You will need to share your expertise in cloud data structures, optimizations, automation, and best practices with the whole of Evinova.

• AWS Data Services: Deep 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 & Leadership: Mentor and guide junior and mid-level engineers, fostering a culture of learning and collaboration. Provide technical leadership in the 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.

• 10+ years in data engineering and data pattern type roles, with significant experience in SaaS and multi-tenant data platforms. Proven track record of mentoring 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: Expert knowledge of S3, RDS, DynamoDB, Kinesis, Glue, Data Zone, Athena, Red Shift Serverless,and AWS Event Bridge.

• Containerization & Orchestration: Deep 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 ability to explain complex technical concepts to…