Evinova isseekinga passionate and experienced Data Foundation Engineering Lead toguideinthe transformation of ourplatform-wide data foundationtoenable our products, data science, andagents to deliver category leading capabilities. Join us inleveragingcutting-edgetechnology, data, and AI to revolutionize life sciences and improve billions of lives globally.
In this pivotal role, you will design, implement, andoptimizerobust cloud-based datalakehouseinfrastructure and operational frameworks that enable rapid innovation and deliver exceptional system reliability.You will beone of the senior-mostengineerswithin the team;
Expected to be hands on,guide,and mentor the team.You will need to share yourexpertisein cloud data infrastructure, automation, and best practiceswith the whole ofEvinova.
Key Responsibilities Infrastructure Design & Management: AWS Data Services: Deep hands-on experience with Lake Formation, Glue (ETL + Catalogue+ Schema Registry), Athena, and at least one ofEMR/ Redshift Serverless. You understand how these compose, not just how each works in isolation.
Open Table Formats: Production experience withS3 Tables, Apache Iceberg (preferred),or Delta Lake. You understand partition evolution, schema evolution, time travel, and compaction — and when eachmatter.
Streaming: Built production streaming pipelines with Kinesis Data Streams or MSK. Comfortable withexactly oncesemantics, 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 isexpected,we currently use GitHub Actions, and some Terraform.
Data Modelling: Can design dimensional models, event schemas, and slowly changing dimensions. Understand the trade-offs betweennormalizedand 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.PySparkor 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 ofthe tooling, patterns, andautomationbest practices.
Collaboration: Partner with cross-functional teams, including product management and security, to aligndata foundationstrategies with business goals and ensure cohesive development and operational workflows.
Required Experience & Qualifications: Experience: 10+ years inData Engineeringroles, withsignificant experiencein SaaS and multi-tenantdataplatforms. Proventrack recordof mentoring team membersindata platformrelated projects.
Cloud Expertise: Strongunderstandingof AWS services, including VPC, IAM, EC2, S3, RDS, Lambda, EKS, AWS WAF, and AWS CloudTrail.
Data Products: Expert knowledge ofS3, RDS,DynamoDB, Kinesis,Glue,DataZone, Athena, RedShift Serverless,and AWSEventBridge.
Containerization & Orchestration: Deepproficiencyin Docker, Kubernetes, Helm, and associated ecosystem tools.
CI/CD Proficiency: Expertisein CI/CD tools such asArgoCDand GitHub Actions.
Infrastructure as Code (IaC): Advanced experience with AWS CDK (TypeScript preferred) and CloudFormation.
Security: Goodknowledge of IAM, AWS KMS, encryption standards, AWS WAF, and security compliance frameworks including NIST.
Monitoring & Alerting: GoodexperiencewithOpenTelemetry, Prometheus, Grafana, AWS CloudWatch, and AWS CloudTrail for monitoring and incident response.
Data & ETL Pipelines: Extensive knowledgewith AWS Glue, AWS Kinesis,and Managed Kafka for real-time and batch data processing.
Programming & Automation: Strong scripting and automation skills using TypeScript and Bash.
Multi-

📌 Data Foundation Engineering Lead - Evinova (Barcelona)
🏢 Astrazeneca
📍 Barcelona