Internship Python Flask Developer Jobs in Corvallis, OR

• Reliability: Data is delivered on time and consistently, with minimal disruptions that impact end users.
• Analytics Readiness: Analytics infrastructure and platforms are well‑designed, accessible, and easy to use, enabling teams to answer questions quickly with fewer data issues or downtime. 
• Impact: Infrastructure supports meaningful business decisions and AI initiatives, demonstrated by increased use of trusted datasets and semantic layers, faster delivery of insights, and improved analytics. 

Design, build, and maintain SQL/dbt data models and semantic layers optimized for analytical query patterns and AI agent access.

Define and maintain semantic layer encoding business logic, KPIs, and metric definitions in a single authoritative location. 

Apply dimensional modeling techniques to accurately represent business processes and metrics in our data warehouse. 

Prepare and curate data assets optimized for machine learning, LLM, and AI-agent workflows.

Partner with analysts, data scientists, and stakeholders to translate business requirements into trusted data/analytical products.

Develop and optimize ELT/ETL pipelines in a cloud data warehouse (e.g., Snowflake) for reliable, scalable data delivery. 

Implement data quality testing (schema, freshness, business rules) and remediate recurring data issues.

Establish and sustain data lineage standards across analytical models, enabling stakeholders to trace data provenance from source to consumption in support of data governance, auditability, and operational reliability.

Contribute to shared analytics codebases via Git/GitHub, including code reviews and established development workflows.

Support Python-based automation, lightweight analytics services, and troubleshooting of cloud or containerized components as needed.

• Solid experience in data modeling, including dimensional modeling concepts and data warehouse design, and leveraging AI tools such as Claude Code.
• Strong SQL proficiency for transformation, analysis, and validation
• Python proficiency for data processing, automation, and integration
• Experience with dbt or a comparable analytics engineering framework to build, test, and document data models

• Experience in Azure cloud environments (AWS or GCP transferable)
• Experience with Snowflake or other modern cloud data warehouses (BigQuery, Redshift, Databricks SQL, etc.)
• Familiarity with relational databases such as SQL Server and PostgreSQL

      Data Platforms & Orchestration

• Experience designing, building, and maintaining ELT/ETL pipelines
• Familiarity with orchestration tools (Airflow, Prefect, Azure Data Factory, or similar)
• Ability to troubleshoot pipeline failures, performance issues, and data quality problems
• Experience implementing data quality tests, including schema, freshness, and business rule validations

• Experience with Git/GitHub for version control and shared codebase collaboration
• Comfortable with following established development workflows, including code reviews   and iterative improvement

• Experience designing data models and semantic layers that support machine-learning and  LLM-based reasoning for leveraging AI-driven analytical agents
• Experience with Tableau or similar BI tools (Power BI, Looker, etc.) to support dashboard    development and analytics
• Familiarity with Python web application development using frameworks such as Flask
• Understanding of web application concepts, including routing, authentication patterns, and   environment configuration
• Exposure to Docker or containerized applications, including interpreting logs and basic  runtime troubleshooting