Data Engineer - Chicago P&C (Remote)

Who We Are

Independent for over 75 years, Milliman delivers market-leading services and solutions to clients worldwide. Today, we are helping companies take on some of the world’s most critical and complex issues, including retirement funding, mortgage and healthcare financing, risk management and regulatory compliance, data analytics and business transformation.

Milliman invests in skills training and career development and gives all employees access to a variety of learning and mentoring opportunities. Our growing number of Milliman Employee Resource Groups (ERGs) are employee-led communities that influence policy decisions, develop future leaders, and amplify the voices of their constituents. We encourage our employees to give back to their varied professions, including leadership in professional organizations. Please visit our web site (https://www.milliman.com/en/social-impact) to learn more about Milliman’s commitments to our people, diversity and inclusion, social impact and sustainability.

Milliman’s Chicago-based Property and Casualty practice is one of the leading experts in P&C insurance matters. Our clients include Fortune 500 corporations, healthcare institutions, privately held companies, public entities, captive insurers and reinsurers. Our cross-discipline team includes actuaries, data scientists, and technologists.

The Opportunity

As an innovator in risk assessment, Milliman embraces technology and builds renowned data-driven tools that evaluate risk for a wide variety of applications. Nodal® is a comprehensive SaaS solution that uses artificial intelligence (AI) and machine learning (ML) to analyze structured and unstructured data to predict high-cost claims. The Nodal product team can assemble data from unstructured sources, build predictive models using cutting-edge machine learning techniques, and implement with experienced claims consultants to ensure successful implementation for our clients.

The Data Engineer is a crucial role on the Nodal team, responsible for designing the development environment of big data architecture for advanced analytics and machine learning to effectively reflect business needs, security requirements, and service level requirements. While our claims data intake processes are automated, the Data Engineer checks for and resolves issues for our existing clients. They are also a key player in new client implementation: ingest the data feed, set up a process to transfer and load into Milliman’s database, and configure our data pipelines. In this role you’ll work independently with a nimble and collaborative team. Just as important as your relevant technical knowledge will be your communication skills, as you’ll be interacting with a variety of Nodal team members and resolving client issues with the utmost urgency.

Responsibilities and expectations:

• Plan, design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading of structured and unstructured data, leveraging big data frameworks such as Apache Spark
• Develop robust and scalable solutions that transform data into a useful format for analysis, enhance data flow, and enable end users to consume and analyze data faster and easier.
• Collaborate with engineers, data scientists, data analysts, product teams, and other stakeholders to translate business requirements into scalable, automated data solution that provide AI-driven insights.
• Develop and maintain the cloud-based data infrastructure on cloud-based platforms such as Databricks or Azure Synapse, optimizing for speed, cost, and scalability
• Manage and optimize data storage solutions, such as data lakes, relational database management systems, and/or data warehouses.
• Assemble large, complex data sets that meet functional and non-functional business requirements.
• Develop and maintain data models, ensuring they align with business objectives and data privacy regulations
• Optimize data workflows and performance by leveraging partitioning, caching, and Spark optimizations to improve processing speed and reduce latency.
• Evaluate and recommend emerging tools and technologies for data infrastructure and processing.
• Maintain comprehensive documentation in Jira and Confluence.
• Perform ad-hoc analyses in SQL and Python to validate data accuracy.

What We Are Looking For

Required:

• 5+ years of professional experience building scalable, cloud-based solutions for ETL (and/or ELT) pipelines, with a strong focus on performance and reliability
• Proficiency in Python, with expertise in designing abstractions, modular code, and reusable components
• Hands-on expertise in Sp