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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Architect Cloud Aws - **Company:** AstraZeneca - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Apache HTTP Server, Software as a Service, Cloud Computing, Cloud Database, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Amazon DynamoDB, Github, Identity and Access Management, Key Management, Machine Learning, Prometheus, Data Streaming, TypeScript, AWS Cdk, Data Logging, Data Classification, Grafana, Apache Spark, Reliability of Systems, Amazon Virtual Private Cloud (VPC), Cloudformation, Amazon Relational Database Service, Data Lakes, Pyspark, Kubernetes, AWS Glue, AWS Data Analytics, Apache Kafka, Data Management, Feature Extraction, Functional Programming, Cloudwatch, Data Pipelines, Serverless Computing, Docker - **Published:** September 29, 2026 - **Apply:** https://www.buscojobs.com.es/data-architect-cloud-aws-en-barcelona-ID-373466436 ## About the Role 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., You will be one of the senior data engineers within our team; You will need to share your expertise in cloud data tools, patterns, optimizations, automation, and best practices with the whole of Evinova.Key ResponsibilitiesInfrastructure 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. Production experience with S3 Tables, Apache Iceberg (preferred), or Delta Lake. 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. 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. 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: Mentorship: Mentor and guide junior engineers and even your peers, fostering a culture of learning and collaboration. 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.RequiredExperience & QualificationsExperience: 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 CloudTrail.Data Products: Strong knowledge of S3, RDS, DynamoDB, Kinesis, Glue, DataZone, Athena, RedShift Serverless, and AWS EventBridge.Strong proficiency in Docker, Kubernetes, Helm, and associated ecosystem tools.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: Good experience with OpenTelemetry, Prometheus, Grafana, AWS CloudWatch, and AWS CloudTrail 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 TypeScript and Bash.Multi-Account AWS Management: Experience managing multiple AWS accounts with AWS Control Tower.Desired Experience & QualificationsAdvanced expertise in AWS CDK, including building complex, reusable constructs and pipelines.Experience with monitoring and logging tools such as Prometheus, Grafana, and AWS CloudWatch.We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. ## Description 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; You will need to share your expertise in cloud data tools, patterns, optimizations, automation, and best practices with the whole of Evinova.Key ResponsibilitiesInfrastructure 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.Production experience with S3 Tables, Apache Iceberg (preferred), or Delta Lake.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.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.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: Mentorship: Mentor and guide junior engineers and even your peers, fostering a culture of learning and collaboration.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.RequiredExperience & QualificationsExperience: 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 CloudTrail.Data Products: Strong knowledge of S3, RDS, DynamoDB, Kinesis, Glue, DataZone, Athena, RedShift Serverless, and AWS EventBridge.Strong proficiency in Docker, Kubernetes, Helm, and associated ecosystem tools.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: Good experience with OpenTelemetry, Prometheus, Grafana, AWS CloudWatch, and AWS CloudTrail 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 TypeScript and Bash.Multi-Account AWS Management: Experience managing multiple AWS accounts with AWS Control Tower.Desired Experience & QualificationsAdvanced expertise in AWS CDK, including building complex, reusable constructs and pipelines.Experience with monitoring and logging tools such as Prometheus, Grafana, and AWS CloudWatch.We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.SummaryLocation: Spain - BarcelonaType: Full time ## 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