Analytics Engineer

Verisk is seeking an Analytics Engineer to help build the trusted data foundation that powers smarter decisions across the insurance ecosystem. In this role, you will partner with product, engineering, data science, and business stakeholders to design scalable data models, transform complex data into reliable analytical assets, and enable high-quality insights for reporting, machine learning, and AI-driven solutions. This is an opportunity to work at the intersection of data, technology, and insurance—turning raw information into governed, reusable, business-ready data products that help Verisk deliver speed, precision, and value to customers.

• 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.
• 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.

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