Software Architect

A Day in the Life

• Architect Scalable Systems: Design robust cloud-native architectures to  support the orchestration of predictive models, and real-time data workflows across a growing analytics product portfolio. 
• AI-Enabled Automation: Spearhead implementation of intelligent agentic  systems to streamline and automate data ingestion, transformation, model  scoring, and decision routing. 
• Data Science Integration: Build and maintain high-throughput pipelines for  predictive model validation, automatic retraining, and model scoring. Optimize a PySpark-based ETL job and deploy your changes via CI/CD pipelines to a cloud environment. 
• Databricks Architecture & Optimization: Design and optimize Databricks workspaces, clusters, and workflows to support large-scale data processing,  machine learning operations, and production model serving. 
• AI-Enabled Data Pipelines: Architect end-to-end ML pipelines within the  Databricks ecosystem, from data ingestion to model deployment and monitoring. 
• Delta Lake Implementation: Leverage Delta Lake for reliable data storage,  versioning, and ACID transactions that support our predictive models and data  services. 
• MLflow Integration: Design systems for model lifecycle management, including  experiment tracking, model registry management, and deployment workflows. 
• Cross-Functional Collaboration: Partner with data scientists, product  managers, and engineers to deliver architecture that balances flexibility,  performance, and innovation. 
• API & Platform Design: Develop GraphQL and REST APIs to expose data  science services to internal platforms and client-facing dashboards. 
• Cloud & Container Expertise: Leverage AWS, Docker, and Databricks to scale  computational resources, optimize model serving, and support high-volume data  operations. 
• Observability & Optimization: Implement system observability practices to  monitor the performance of predictive workflows in production. 

Role Progression

• Within 1 month, you’ll: Onboard into Encoura's platforms and understand current products andinfrastructure, including the Insights Cloud and analytics toolset. 
• Within 3 months, you’ll: Lead architectural support for production data science environments, with contributions to new workflows and enhanced data processing systems. 
• Within 6 months, you’ll: Architect and deploy new AI-native systems to support productized predictive intelligence across client solutions like Opportunity Model and Financial Aid Optimization. 
• Within 1 year, you’ll: Serve as the go-to architect for Encoura’s next-gen data products, leading the shift toward scalable, self-improving systems that differentiate our portfolio in the market. 

About You

• You are passionate about turning data science into deployable, scalable software products. 
• You are a self-starter who thrives in ambiguous problem spaces and loves architecting clean, modular systems. 
• Hands-on with Databricks, or equivalent Spark-based data processing environments. 
• Python (advanced) and associated libraries: Pandas, PySpark, Polars, NumPy. 
• Proven experience designing data pipelines and backend systems for ML/AI workflows. 
• Cloud platforms, especially AWS (S3, EC2, ElastiCache, Lambda, SageMaker, Glue, Athena, etc.) and Kubernetes. 
• Containerization with Docker; orchestration experience a plus (e.g. we standardize on Kubernetes). 
• Familiarity with Streamlit, GitHub workflows, and CI/CD best practices. 
• Expertise in development and exposing ML models with REST and/or GraphQL API. 
• Experience in higher ed, enrollment tech, or education analytics is a plus but not required. 
• Knowledge of large-scale model management, ML ops, and distributed preferred computing. 
• Exposure to business intelligence, cost optimization strategies, or institutional research domains preferred. 

What Sets Us Apart

• Mission driven culture 
• Comprehensive health and benefits package 
• 401k company match that vests immediately upon participation 
• Paid holidays and a generous PTO policy 
• Paid parental leave