Data Foundation Engineering Lead - Evinova

La oferta

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 agent s 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 engineer s within the team ; expected to be hands on, guide , and m entor the team . You will need to shar e your expertise in cloud data infrastructure, automation, and best practices with the whole of Evinova . Key Responsibilities Infrastructure Design & Management: AWS Data Services: Deep hands-on experience with Lake Formation, Glue (ETL + Catalog ue + 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 normali z ed and denormali z ed 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 & 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 & Qualifications: 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 . 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. 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…