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 agent s 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, i mplement, and optimize robust cloud-based data within the lakehous e , catalogue , pipelines , and operational frameworks that enable rapid innovation and deliver exceptional system reliability . You will be one of the senior - most data architec ts and engineers within the data foundation team ; expected to be hands on, guide , and mentor the team . You will need to shar e your expertise in cloud dat a structures , optimizations, automation, and best practices with the whole of Evinova . Key Responsibilities Infrastructure Design & Management AWS Data Services: Deep hands-on experience with Lake Formation, Glue (ETL &#43; Catalog ue &#43; 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 backpressure . Infrastructure as Code: AWS CDK ( TypeScript ) or CloudFormation. You define infrastructure in code, not in the console. CI/CD for data pipelines is expected , we currently use GitHub Actions, and some Terraform . Data Modelling: Can design dimensional models, event schemas, and slowly changing dimensions. Understand the trade-offs between normali z ed and denormali z ed 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. Required Experience & Qualifications 10 &#43; years in d ata e ngineering 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 CloudTrail. Data Products: Expert knowledge of S3, RDS, DynamoDB, Kinesis, Glue, DataZone , Athena, RedShift Serverless, and AWS EventBridge . Containerization & Orchestration: Deep proficiency in Docker, Kubernetes, Helm, and associated ecosystem tools. CI/CD Proficiency: Expertise in CI/CD tools such as ArgoCD and GitHub Actions. Infrastructure as Code ( IaC ): Advanced experience with AWS CDK (TypeScript preferred) and CloudFormation. Security: Good knowledge of IAM, AWS KMS, encryption standards, AWS WAF, and security compliance frameworks including NIST. Monitoring & Alerting: </