Only on W2 Role: No C2C
Title/Level: Data Analyst
Location: Chicago IL (Remote)
Duration: 6 months (20 hours per week - 4x5)
1. Project Overview
The purpose of the Dispute Modeling Engine (DME) will be to provide a granular view into historical usage data and will support analysts when reviewing disputes submitted by the customer.
The DME will identify changes (both internally and directly on the customers instance) that may have occurred that are impacting usage and subsequently quantify a valid credit amount.
The DME will leverage AI to provide key insights into the data such as changes that have occurred identifying anomalies etc.
2. Technical Environment
Data Source: Snowflake (various tables extracted from customers instance as well as internally managed tables)
Visualization Tool: Power BI
- AI Integration: Snowflake Cortex AI include AI summarization of changes
Data Modeling & Integration
- Connect Power BI to Snowflake efficiently
- Optimize queries and warehouse performance.
- Create semantic models and reusable datasets.
- Integrate AI tools into dashboard
Dashboard Development
- Develop dashboard that dynamically changes visuals and data sources based on products selected
- Implement an interactive dashboard that breaks out details by product type along with drill-through and filter capabilities.
- Establish consistent visual design KPI cards and navigation.
- Integrate AI capabilities that can assess data from various tables to provide key insights on data in a dashboard
Cortex AI Integration
- Incorporate AI Summarization of changes
- Implement natural language Q&A or conversational insights using Cortex LLM features.
- Automate anomaly or trend detection.
Performance Optimization
- Ensure efficient load times DAX optimization and query performance.
- Set up caching incremental refresh or performance monitoring.
Documentation & Knowledge Transfer
- Provide clear technical documentation (data model DAX logic AI pipelines).
- Conduct knowledge transfer or walkthrough sessions.
Power BI Expertise
- Advanced DAX Power Query data modeling and visualization design.
Snowflake Expertise
- Proficiency in SQL schema design and performance tuning.
- Familiarity with Snowflake and integration into PowerBI
AI/ML & Cortex AI Familiarity
- Experience using Snowflake Cortex (or equivalent LLM integration like Vertex AI or Azure OpenAI).
- Understanding of prompt engineering model outputs and security/privacy considerations.
General Skills
- Experience integrating BI with AI/ML workflows.
- Strong communication and documentation habits.
- Ability to work iteratively with non-technical stakeholders.
Education: Bachelors degree in Data Science Computer Science Information Systems Statistics Engineering or a related quantitative field; equivalent practical experience accepted.
Coursework or formal training in: Data modeling SQL and cloud data warehousing
Business Intelligence / data visualization
Machine learning and LLM fundamentals (preferred)
Relevant certifications such as: Microsoft PL-300 (Power BI Data Analyst)
Snowflake SnowPro Core or Advanced
AI/LLM certifications (Azure OpenAI Vertex AI or equivalent) are a plus.
Demonstrated completion of applied data engineering BI or AI bootcamps is also acceptable.
Only on W2 Role: No C2C Title/Level: Data Analyst Location: Chicago IL (Remote) Duration: 6 months (20 hours per week - 4x5) 1. Project Overview The purpose of the Dispute Modeling Engine (DME) will be to provide a granular view into historical usage data and will support analysts when reviewing dis...
Only on W2 Role: No C2C
Title/Level: Data Analyst
Location: Chicago IL (Remote)
Duration: 6 months (20 hours per week - 4x5)
1. Project Overview
The purpose of the Dispute Modeling Engine (DME) will be to provide a granular view into historical usage data and will support analysts when reviewing disputes submitted by the customer.
The DME will identify changes (both internally and directly on the customers instance) that may have occurred that are impacting usage and subsequently quantify a valid credit amount.
The DME will leverage AI to provide key insights into the data such as changes that have occurred identifying anomalies etc.
2. Technical Environment
Data Source: Snowflake (various tables extracted from customers instance as well as internally managed tables)
Visualization Tool: Power BI
- AI Integration: Snowflake Cortex AI include AI summarization of changes
Data Modeling & Integration
- Connect Power BI to Snowflake efficiently
- Optimize queries and warehouse performance.
- Create semantic models and reusable datasets.
- Integrate AI tools into dashboard
Dashboard Development
- Develop dashboard that dynamically changes visuals and data sources based on products selected
- Implement an interactive dashboard that breaks out details by product type along with drill-through and filter capabilities.
- Establish consistent visual design KPI cards and navigation.
- Integrate AI capabilities that can assess data from various tables to provide key insights on data in a dashboard
Cortex AI Integration
- Incorporate AI Summarization of changes
- Implement natural language Q&A or conversational insights using Cortex LLM features.
- Automate anomaly or trend detection.
Performance Optimization
- Ensure efficient load times DAX optimization and query performance.
- Set up caching incremental refresh or performance monitoring.
Documentation & Knowledge Transfer
- Provide clear technical documentation (data model DAX logic AI pipelines).
- Conduct knowledge transfer or walkthrough sessions.
Power BI Expertise
- Advanced DAX Power Query data modeling and visualization design.
Snowflake Expertise
- Proficiency in SQL schema design and performance tuning.
- Familiarity with Snowflake and integration into PowerBI
AI/ML & Cortex AI Familiarity
- Experience using Snowflake Cortex (or equivalent LLM integration like Vertex AI or Azure OpenAI).
- Understanding of prompt engineering model outputs and security/privacy considerations.
General Skills
- Experience integrating BI with AI/ML workflows.
- Strong communication and documentation habits.
- Ability to work iteratively with non-technical stakeholders.
Education: Bachelors degree in Data Science Computer Science Information Systems Statistics Engineering or a related quantitative field; equivalent practical experience accepted.
Coursework or formal training in: Data modeling SQL and cloud data warehousing
Business Intelligence / data visualization
Machine learning and LLM fundamentals (preferred)
Relevant certifications such as: Microsoft PL-300 (Power BI Data Analyst)
Snowflake SnowPro Core or Advanced
AI/LLM certifications (Azure OpenAI Vertex AI or equivalent) are a plus.
Demonstrated completion of applied data engineering BI or AI bootcamps is also acceptable.
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