As a Lead Analytics Engineer you will create highly-scalable production data pipelines for our advanced analytics and data science teams - as well as perform ad-hoc analyses for personnel in less technical functions - to use every day to evolve our business. The Lead Analytics Engineer will design, evaluate, and test data infrastructures and be a subject matter expert for all things data across the organization.
Essential Job Functions:
Work closely with the Data Science team to perform complex analytics and data preparation tasks
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across all departments, all products, and all states.
Solve our most challenging data integration problems, utilizing optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources.
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts.
The Lead Analytics Engineer is an expert in all datalakes, data warehouses, and data cubes within Mercury, with no gaps in knowledge. Can efficiently and accurately extract and manipulate data from any source.
Collaborate with teams of data analysts and data scientists, who research and integrate algorithms to develop solutions to address complex data problems. Influence all functions across the organization to identify data opportunities to drive profitable growth. Proactively identify pain points that Analytics & Data Science face with our existing data models.
Leverages existing data infrastructure to fulfill all data-related requests, perform necessary data housekeeping, data cleansing, normalization, hashing, and implementation of required data model changes. Analyzes data to spot anomalies, trends and correlate similar data sets. Designs, develops and implements natural language processing software modules.
Other functions may be assigned
• Bachelor's degree in Computer Engineering, Computer Science, Mathematics, Electrical Engineering, Information Systems, or related field
• Or equivalent combination of education and/or experience
5 or more years of experience in data analytics, analytics engineering, data engineering, and/or data science.
3 or more years experience in P&C insurance, preferred, but not required
Knowledge and Skills:
• Experience working with SQL and Python to build data pipelines and perform analyses
• Experience with AWS (S3, EC2), Unix/Shell scripting
• ETL/ELT experience: e.g. dbt, informatica, fivetran, etc.
• Experience with a variety of relational and non-relational databases and data sources
• Familiarity with data pipeline orchestration frameworks and related tools a plus (e.g. dbt)
• Familitiary with CI/CD best practices
• Expert data skills and the ability to work with large structured and unstructured data sources
• A high-level specialist who regularly interacts and works with senior management.
• Expert at analyzing data to identify gaps and inconsistencies
• The ability to think conceptually, analytically and creatively; comfortable with ambiguity.
• Experience managing and communicating data plans and data models to internal clients.
• Demonstrated solid understanding, and passion for, all areas of data/analytics engineering best practices.
• Demonstrated expert skills in data mining and data analytics
• Solid experience with cloud-based advanced data and analytics environment
• Excellent problem-solving skills required
• Excellent analytical and critical thinking required
• Excellent written and verbal communication skills required
• Demonstrate Company's Core Values