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Analytics Engineer
Imperial PFS
- Edwardsville, Illinois, United States
- Edwardsville, Illinois, United States
About
This prime reason for this role's existence is to bridge the gap between raw data ingestion and analytics needs, ensuring that our analysts and stakeholders have access to high-quality, well-documented, and performant data models. This position directly contributes to the company's overall mission by enabling data-driven decision making across all departments, improving operational efficiency, and supporting strategic initiatives through reliable analytics infrastructure.
Key contributions to the company include:
Building standardized data models that reduce time-to-insight for business users
Implementing data quality and governance frameworks that ensure information is accurate and compliant.
Creating reliable, well-documented data pipelines that enable consistent reporting and analytics across all business functions
What You'll Do Data Modeling and Pipeline Development Design, build, and manage data models and ELT/ETL pipelines to transform raw data into structured formats within Snowflake using dbt
Create conform and analytics layers that standardize data for business consumption
Develop dimensional models and data marts tailored to business requirements
Data Quality and Governance Implement best practices for data quality, integrity, and performance monitoring
Contribute to data governance frameworks, including maintaining data lineage and definitions
Establish data quality checks and validation processes within dbt workflows
Performance Optimization Optimize data storage and retrieval processes within Snowflake to ensure scalable, reliable, and cost-effective data solutions
Fine-tune SQL queries and data transformations for optimal performance
Monitor and improve pipeline efficiency and resource utilization
Technical Documentation Create and maintain clear, comprehensive documentation for data models, processes, and key metrics
Document data lineage and maintain metadata for analytical datasets
Establish documentation standards and best practices for the team
Software Engineering Practices Leverage version control and CI/CD pipelines for streamlined, reliable development processes
Ensure code quality through testing, peer reviews, and automated deployment
Collaboration and Stakeholder Management Work closely with analysts and business subject matter experts to understand requirements
Translate business needs into effective technical data solutions
Participate in regular stakeholder meetings to refine and expand data capabilities
What You Bring Required Qualifications
2-4 years of experience in analytics engineering, data engineering, or similar role
Advanced SQL proficiency in ANSI-SQL for querying, data transformation, and building data infrastructure
Snowflake expertise with hands-on experience in its architecture and best practices for data management and processing
Strong data modeling skills with deep understanding of data modeling principles and experience designing efficient structures for analytics
dbt proficiency for managing data transformations and building data models within Snowflake
Passion for detail and quality - skilled at spotting data and process gaps and committed to driving continuous improvement
Strong communication skills and comfort collaborating with non-technical stakeholders
Intellectual curiosity and drive to understand business problems through data
Preferred Qualifications
SnowPro certification or equivalent advanced Snowflake expertise
Experience with modern cloud data architecture (data lakes, data lakehouses, cloud data platforms)
Experience with ELT/ETL tools (Fivetran experience a plus)
Knowledge of data visualization tools (Tableau, Looker, Power BI)
Previous experience in insurance, finance, or regulated industries
Python programming experience for data parsing, transformation, and scripting within data pipelines
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Languages
- English
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