Data Engineer II, AWS, Python, SQL

Own and deliver data pipeline development and ongoing support for Property and GL pricing use cases Lead model implementation for Property and GL pricing models in partnership with Data Science and Actuarial teams Build and serve as the GL data subject matter expert to support benchmarks, modernization initiatives, and future enhancements Provide project leadership, planning, and execution across Property and GL data engineering initiatives Execute complex data preparation activities, including exploration, cleansing, and transformation, with awareness of enterprise architecture, platforms, and downstream consumption patterns Translate actuarial and data science requirements into scalable, production-ready data solutions Adopt and embed MLOps practices across the model development and implementation lifecycle Establish data quality controls, profiling, and monitoring for model implementation and ongoing performance Present analysis and technical recommendations to influence data architecture and implementation decisions Provide technical leadership through code reviews, design guidance, and mentoring within the team Apply agile methodologies to plan, prioritize, and deliver work across concurrent initiatives Perform other duties as assigned Requirements Bachelor's Degree in STEM related field or equivalent. Eight years of related experience. Deep, hands-on experience with modern engineering tools and practices, including: Cloud platforms (preferably AWS) and model implementation in the cloud Programming in Python and SQL Working with data engineering concepts and building/maintaining data pipelines AI-native solutions and modern software engineering practices (e.g., APIs, microservices, test automation) Proven ability to deliver high-quality solutions at a steady, predictable pace: Breaks work into small, releasable increments Delivers complete, robust solutions while effectively managing tradeoffs Demonstrated domain expertise, including: Strong understanding of relevant technical concepts and industry trends In-depth knowledge of the systems you've worked on and familiarity with adjacent systems Strong problem-solving skills with a focus on building resilient, long-lived systems and finding innovative ways to resolve issues. Excellent written and verbal communication skills, with the ability to collaborate effectively with engineers, product partners, and business stakeholders. Proven experience leading or mentoring other engineers and helping to create a safe, inclusive environment where others can learn and grow. Self-motivated, with a track record of proactively identifying opportunities, driving improvements, and following through on team efforts. Preferred, Not Required: Experience in or exposure to the insurance industry, or a strong desire to learn the domain. Experience with Infrastructure as Code and DevOps tooling, such as Terraform and CI/CD pipelines. Core Competencies Demonstrates expertise in data pipeline development and model implementation, with a strong focus on cloud platforms, particularly AWS. Proficient in Python and SQL, with a commitment to applying MLOps practices and agile methodologies to deliver high-quality, scalable data solutions. Highest-signal resume keywords Data Pipeline Development Cloud Platforms (AWS) Python Programming SQL Programming MLOps Practices ATS Optimization Keywords Hard Skills Data Engineering Concepts Model Implementation Data Preparation Activities AI-Native Solutions Test Automation Infrastructure as Code DevOps Tooling APIs Microservices Data Quality Controls Soft Skills Problem-Solving Skills Excellent Communication Skills Leadership Mentoring Self-Motivated Industry Keywords Insurance Industry Actuarial Data Science Modern Software Engineering Practices Benchmarking Tools & Technologies Terraform CI/CD Pipelines Cloud Platforms Data Architecture Enterprise Architecture J-18808-Ljbffr