Overview:We are seeking a Retail Actuarial Data Engineer to join our Actuarial Data function. This person will lead the data governance and continuous improvement of a modern Actuarial Data platform supporting Retail reporting and analytics. Responsible for managing data quality, stability and usability of Retail data for modeling, valuation, and other downstream processes as the Retail business scales. The selected candidate will work in a hybrid in-office arrangement out of one of our office locations in Hartford, CT., or Charlotte, N.C.
Responsibilities:The Retail Actuarial Data Engineer will have end-to-end ownership of Actuarial Retail Data Platform which entails:
Act as a liaison between Actuarial and other functions like IT, Finance, and Operations, ensure communication and alignment across multiple forumsOversee the full Retail data lifecycle across source admin systems Retail Datalake Actuarial Retail Lakehouses business user destinationsGather and define business and technical requirements for additional enhancements to our suite of products as well as high value capabilities on Retail's strategic business planMaintain an understanding of overall architecture and own the evolution of the Retail Actuarial Data Platform, driving improvements in scalability, reliability, and usability for business-critical processesMonitor daily data quality to promote complete and accurate data for various reporting purposes while proactively communicating any failures and resolutions to stakeholdersEscalate and prioritize data defects and enhancements as they ariseEnhance control frameworks and implement review processes in an efficient and automated mannerShape how Retail Actuarial data is governed, validated, and delivered to enable faster actuarial analysis and decision-making
Qualifications:
Bachelor's degree in a quantitative fieldMinimum of 3 years of relevant experience in actuarial data, data engineering, data science, or related fields within the insurance industryStrong proficiency with SQL and relational database concepts; familiarity with data engineering tools and ETL concepts; comfortable building complex queries and proceduresExperience using AI tools and models in various work applications and contextsExperience with scripting languages (e.g., Python/PySpark) or data visualization tools (e.g., Power BI) is preferredFamiliarity with actuarial processes and the types of data used in actuarial modeling, valuation, and experience studies (e.g., policyholder data, transaction data, input/output data)Actuarial exams or studies a plusAbility to work independently and collaboratively in a fast-paced, technical environment; Excellent problem-solving and communication skills