Data & Analytics Engineer at Peterson

Compensation

$105,000 – $125,000/yr

Location: San Leandro, California

Description

The Data & Analytics Engineer at Peterson supports and evolves the enterprise analytics platform by performing data engineering, analytics delivery, and reporting. The role focuses on modern cloud data platforms like Snowflake and Microsoft Fabric, utilizing Power BI for reporting, and implementing AI-enabled workflows to enhance efficiency, quality, and scalability while ensuring data accuracy and governance.

Required Skills

Data Engineering
Analytics Delivery
Reporting
Cloud Data Platforms
Layered Data Architecture
Power BI Reporting
AI Enabled Workflows
Snowflake
Microsoft Fabric
SQL Server Integration Services (SSIS)
Azure Data Factory
Python
ETL Troubleshooting and Optimization
Data Validation, Reconciliation, and Impact Analysis
Data Quality, Reconciliation, and Documentation Standards
Enterprise Resource Planning (ERP) Platforms
Microsoft Dynamics
Data Pipelines, Transformation Patterns, and Data Models
Data and Analytics Standards, Best Practices, and Frameworks
Business, Finance, and IT Teams

Additional Information

Required Previous Experiences: Three (3) years of directly related experience in data engineering, business intelligence, or analytics roles.
Hands-on work in modern cloud data platforms (e.g., Snowflake, Microsoft Fabric, or similar), including ETL/ELT ingestion and transformations.
Experience with Python for data engineering, automation, and AI-enabled workflows.
Hands-on experience building production reports and dashboards (Power BI required; other BI tools acceptable).
Experience working with Microsoft integration tools, including SSIS and/or Azure Data Factory.
Experience supporting end-to-end data pipelines, from ingestion through analytics and reporting.
Preferred Qualifications: Modern cloud data platforms (e.g., Microsoft Fabric, Snowflake, or similar), with the ability to work across evolving architectures.
Finance domain knowledge (GL, revenue, costs, contracts).
Microsoft Dynamics or ERP data migration experience.
Working within the Microsoft ecosystem / Microsoft 365.
Spark/PySpark or notebook-based data processing for advanced transformations.