Data Foundation Engineering Lead - Evinova
Barcelona, CT, ES
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingDesign, implement and optimize cloud-based data lakehouse infrastructure
Mentor and guide junior and mid-level engineers
Build and run production streaming data pipelines
Define infrastructure as code and build CI/CD for data pipelines
Partner with product management and security on data foundation strategy
From the employer’s posting
design, implement, and optimize robust cloud-based data lakehouse infrastructure
Mentor and guide junior and mid-level engineers
Built production streaming pipelines with Kinesis Data Streams or MSK
You define infrastructure in code, not in the console. CI/CD for data pipelines is expected
Partner with cross-functional teams, including product management and security
Tools in this posting
- Bash
- Python
- Scala
- TypeScript
- AWS
- Delta Lake
- Docker
- DynamoDB
- Grafana
- Iceberg
- Kafka
- Kubernetes
- Redshift
- S3
- Spark
- Terraform
- PySpark
Source — Tool mentions in context
* 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.
* 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.
* 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.
* 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.
Infrastructure Design & Management: * AWS Data Services: Deep 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.
* Experience: 10+ years in Data Engineering 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* AWS Data Services: Deep 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.
* 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.
Posting record
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- Live
- First seen by us
- Oct 2, 2026 · 27 days ago
- Recorded sightings
- 157
- Last seen by us
- Oct 29, 2026
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Barcelona, CT, ES
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17-dic-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.
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