Data Engineering Architect - Evinova

Evinova, a healthtech leader, is seeking a passionate and experienced Data Engineering Architect to guide in the structure of the structure our platform-wide conformed data within 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 design, implement, 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-most data architects and engineers within the data foundation team; expected to be hands on, guide, and mentor the team. You will need to share your expertise in cloud data structures, optimizations, 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 + 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 & 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 

• 10+ years in data engineering and data pattern type 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.