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Verisk

Analytics Engineer

Verisk

Analytics Engineer transforming raw data into structured datasets for analysis and machine learning. Collaborating with teams to ensure data integrity and accessibility.

Posted 4/15/2026full-timeRemote • Utah • 🇺🇸 United StatesMid-LevelSenior💰 $100,000 - $140,000 per yearWebsite

Tech Stack

Tools & technologies
AirflowAmazon RedshiftApacheCassandraETLMongoDBMySQLNoSQLPostgresPythonSQL

About the role

Key responsibilities & impact
  • 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.

Requirements

What you’ll need
  • 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.

Benefits

Comp & perks
  • Health Insurance
  • Retirement Plan
  • Disability benefits
  • Paid Time Off program

ATS Keywords

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Hard Skills & Tools
data modelingdata warehousingSQLPythonETLApache AirflowLuigiDBTPostgreSQLMySQL
Soft Skills
communicationcollaboration