Senior Data Engineer - Evinova

Posted 2 Days Ago
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Barcelona, Cataluña, ESP
In-Office
Senior level
Biotech • Pharmaceutical
The Role
Build and optimize AWS-based data platforms, lakehouses, catalogs, streaming and ETL pipelines, infrastructure automation, governance, security, and monitoring. Design data models and production-grade cloud frameworks using AWS services, Apache Iceberg or related table formats, Spark, and infrastructure as code. Mentor engineers, establish engineering best practices, and collaborate with product and security teams to deliver reliable multi-tenant SaaS data capabilities.
Summary Generated by Built In

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

17-sept-2026

Closing Date

30-sept-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.

Skills Required

  • 7+ years of hands-on data engineering experience
  • Strong experience with SaaS and multi-tenant data platforms
  • Experience mentoring team members on data platform projects
  • Strong AWS expertise, including VPC, IAM, EC2, S3, RDS, Lambda, EKS, WAF, and CloudTrail
  • Knowledge of AWS S3, RDS, DynamoDB, Kinesis, Glue, DataZone, Athena, Redshift Serverless, and EventBridge
  • Production experience with open table formats such as S3 Tables, Apache Iceberg, or Delta Lake
  • Production streaming pipeline experience with Kinesis Data Streams or Managed Kafka
  • Proficiency with Docker, Kubernetes, Helm, and related ecosystem tools
  • Expertise with CI/CD tools such as ArgoCD and GitHub Actions
  • Advanced AWS CDK experience, preferably using TypeScript, and CloudFormation experience
  • Knowledge of IAM, AWS KMS, encryption standards, AWS WAF, and NIST security frameworks
  • Experience with OpenTelemetry, Prometheus, Grafana, AWS CloudWatch, and AWS CloudTrail
  • Extensive AWS Glue, AWS Kinesis, and Managed Kafka experience for batch and real-time processing
  • Strong automation and scripting skills using TypeScript and Bash
  • Experience managing multiple AWS accounts with AWS Control Tower
  • Exceptional verbal and written communication skills
  • Experience building complex, reusable AWS CDK constructs and pipelines
  • Experience with Prometheus, Grafana, and AWS CloudWatch monitoring and logging
  • Exposure to multi-tenant SaaS platform best practices
  • Experience with AI tools and frameworks

AstraZeneca Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.

  • Fair & Transparent Compensation Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
  • Strong & Reliable Incentives Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
  • Retirement Support A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.

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The Company
HQ: Gaithersburg, MD
70,000 Employees
Year Founded: 1999

What We Do

We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet.

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