As a Data Engineer, you will be responsible for transforming raw data from our applications into structured datasets for large-scale analysis and machine learning model training. 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 a Data Engineer or in a similar role.Technical Proficiency:Programming Languages: Proficiency in Python, SQL, and familiarity with languages such as C# or Java.Data Processing: Experience with ETL tools and frameworks (e.g., Apache Airflow, Luigi, DBT).Big Data Technologies: Hands-on experience with big data technologies such as Hadoop, Spark, and Kafka.Database Management: Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).Cloud Platforms: Experience with cloud services (e.g., AWS, Google Cloud, Azure) and their data processing tools (e.g., AWS Glue, Google BigQuery).AI: Familiarity and enthusiasm for bleeding-edge analytical enablement using tools such as Large Language Models and Prompt Engineering. Data Warehousing: Knowledge of data warehousing concepts and solutions (e.g., Redshift, Snowflake).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. 

Data Pipeline Development: Design, develop, and maintain scalable data pipelines to process raw data from various sources.

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 data pipelines, processes, and schemas.

Working Conditions:

40 hours per week, with occasional, but rare, overtime

Remote / Hybrid / Flexible Work Options Available

Frequent interaction with developers, test automation engineers, QA, management, and members of other Verisk subsidiaries

Some days we just leave the office and have fun team building activities!

Regular team lunches!

State of the art facility with basketball, volleyball, and gym

Ping pong, foosball, fruit bowls, snacks

Fun and energetic teams

Time for innovation, Hack-A-Thons, and learning