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.

Are you ready to shape the future of healthcare?

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.

Key Responsibilities

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 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 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 CloudTrail.

Data Products: Strong knowledge of S3, RDS, DynamoDB, Kinesis, Glue, DataZone, Athena, RedShift Serverless, and AWS EventBridge.

Containerization & Orchestration: Strong 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: 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.

Communication & Collaboration: Exceptional verbal and written communication skills, with the ability to explain complex technical concepts to diverse stakeholders.

Desired Experience & Qualifications

Advanced expertise in AWS CDK, including building complex, reusable constructs and pipelines.

Experience with monitoring and logging tools such as Prometheus, Grafana, and AWS CloudWatch.

Exposure to multi-tenant SaaS platforms and best practices.

Experience working with AI tools and frameworks.

Personal Attributes

Big Picture: Able to understand the strategic direction and help architect smaller initiatives with the direction in mind.

Mentor & Leader: Enjoys mentoring team members, and fostering a collaborative, innovation-driven team culture.

Organized & Adaptable: Able to manage multiple priorities and thrive in a fast-paced environment.

Innovative: Passionate about leveraging technology to solve complex problems and drive efficiency.

Customer-Focused: Dedicated to building infrastructure that delivers measurable business and customer value.

Date Posted

05-oct-2026

Closing Date

14-oct-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.