Data and Systems Engineer

Job Responsibilities:

• • Support development of reference architectures for data, analytics, and compute platforms.
• • Ideate, develop, and document system design architecture and define and refine technical standards as needed.
• • Assist with modeling data flows, source-to-target mappings, and governance frameworks.
• • Participate in design reviews for Databricks, AWS, and Posit platform capabilities.
• • Support development and optimization of ETL/ELT pipelines in Databricks with Unity Catalog and Delta Lake.
• • Assist with AWS architecture including S3, Glue, IAM, Lambda, EC2, EMR, and CloudWatch.
• • Contribute to integrations between Databricks, Posit Workbench/Connect, and scientific data platforms.
• • Support CI/CD and IaC workflows using Terraform, GitHub Actions, and AWS-native tooling.
• • Support data governance initiatives including Unity Catalog permissions and lineage tracking.
• • Assist with platform security reviews, compliance controls, and audit readiness.
• • Contribute to documentation of architectural standards and operational safeguards.
• • Collaborate with scientists, engineers, and platform teams to understand requirements.
• • Support onboarding, training, and documentation for Databricks, AWS, and Posit users.
• • Participate in platform roadmap discussions and capability assessments.

Skills:

• • Hands-on experience with Databricks (Unity Catalog, Delta Lake, clusters, notebooks).
• • Knowledge of Posit Workbench/Connect/Package Manager and R/Python workflows.
• • Experience with AWS services: S3, IAM, Lambda, EC2, Glue, EMR, CloudWatch.
• • Ability to develop automation and workflows using Python, SQL, R, Bash, or PowerShell.
• • Understanding of cloud architectures, data engineering patterns, and system integrations.
• • Hands-on modeling experience creating architecture diagrams (Visio, Draw.io, SqlDBM).
• • Exposure to Terraform, CloudFormation, or GitHub Actions.
• • Familiarity with scientific computing environments or R&D workflows on a global scale.

Education/Experience:

• Candidates should possess a bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or a closely related discipline, or demonstrate equivalent professional experience in a relevant technical field.