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…