Data Foundation Engineering Lead - Evinova

Empresa: AstraZeneca
Provincia: Barcelona
Población: 
Descripción: Evinova is seeking a passionate and experienced Data Foundation Engineering Lead to guide in the transformation of our platform-wide 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 design, implement, and optimize robust cloud-based data lakehouse infrastructure and operational frameworks that enable rapid innovation and deliver exceptional system reliability. You will be one of the senior-most engineers within the team; expected to be hands on, guide, and mentor the team. You will need to share your expertise in cloud data infrastructure, automation, and best practices with the whole of Evinova. Key Responsibilities Infrastructure Design and 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. - 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 and Machine Learning: Exposure to AI tools and frameworks is a plus. - Mentorship and Leadership: Mentor and guide junior and mid-level engineers, fostering a culture of learning and collaboration. Provide technical leadership in the 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 and Qualifications: - Experience: 10+ years in Data Engineering roles, with significant experience in SaaS and multi-tenant data platforms. Proven track record of mentoring team member...
Tecnologías: AWS, Glue, Athena,
Tipo de Contrato: Indefinido
Salario: Sin especificar
Experiencia: 
Funciones: Big Data
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