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Analytics Engineer

Job

Verisk Analytics, Inc

Lehi, UT (In Person)

Full-Time

Posted 6 weeks ago (Updated 2 weeks ago) • Actively hiring

Expires 6/14/2026

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Job Description

As an Analytics Engineer, you will be responsible for transforming raw data from our applications into structured datasets for large-scale analysis and machine learning models. You will work closely with our development, data science, and business intelligence teams to ensure data integrity, quality, and accessibility.
Educational Background:
Bachelor's degree in computer science, Data Engineering, or a related field.
Experience:
3+ years of experience as an Analytics Engineer or in a similar role.
Strong Communication skills:
Ability to work with technical and non-technical audiences to translate business requirements into data models.
Technical Proficiency:
Data Warehousing:
Knowledge of data warehousing concepts and solutions (e.g., Redshift, Snowflake).
Data modeling:
experience in modern data modeling practices, ideally dimensional modeling.
Programming Languages:
Proficiency in SQL and a familiarity with Python.
Data Processing:
Experience with ETL tools and frameworks (e.g., Apache Airflow, Luigi, DBT).
Database Management:
Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).
Version Control:
Proficient with version control systems (e.g., Git).
Machine Learning:
Understanding of machine learning concepts and experience working with data for ML model training.
AI:
Familiarity and enthusiasm for bleeding-edge analytical enablement using tools such as Large Language Models and Prompt Engineering.
Data Modeling :
Research and work with business stakeholders to develop our data warehouse model.
Data Transformation:
Clean, transform, and enrich data to create high-quality datasets suitable for analysis and machine learning.
Collaboration:
Work closely with product teams, software developers, data scientists, and analysts to understand data needs and deliver innovative solutions.
Data Management:
Ensure data accuracy, consistency, and reliability across all datasets.
Optimization:
Optimize data processes for performance and scalability.
Documentation:
Maintain comprehensive documentation of transformation logic and lineage.