• Support the migration of data sources into production environments while ensuring the accuracy, reliability, and integrity of data processes • Contribute to a strategic initiative focused on ingesting and transforming various BDX data sources • Apply robust validation and quality-control logic • Deliver meaningful claims analytics and insights to business stakeholders • Validate and ensure the reasonability of data decisions, including handling duplicate claims and identifying line of business and claims-made vs. occurrence data • Review fidelity of validation and configuration logic: confirm appropriate columns are checked, ensure configurations are logical and functional, verify validation failures are handled correctly • Identify data anomalies in policy or related datasets and escalate to appropriate teams

• Oversee the entire data pipeline from instrumentation to presentation • Work directly with departments and senior leadership to decide what to gather and how to analyze it • Identify KPIs around engagement, retention, and churn, and design pipelines to collect them • Own the databases: schema, performance, health • Be hands-on with the ETL pipelines and the infrastructure supporting it • Automate reporting and create dashboards for a variety of audiences • Play the game

• Build and deploy scalable ML/AI and optimization models to enhance demand forecasting, optimize capacity allocation, and develop user-specific feature engineering for real-time cloud gaming services. • Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes. • Acquire and apply domain knowledge of the product and software stack to identify and drive the resolution of data inconsistencies and improve model performance, especially in the context of optimization outcomes. • Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions. • Design and implement improvements to real-time prescriptive scheduling pipelines, using techniques like linear programming and constraint optimization, to enhance capacity utilization and user retention. • Improve productivity of the organization by mining petabytes of data for actionable insights for business and engineering, often through prescriptive recommendations. • Collaborate with a variety of partners to understand requirements, design robust solutions, and guide the team to deliver impactful results. • Leverage agentic AI to deliver best-in-class automation and programming solutions for complex analytical problems.

• Investigate customer questions about data displayed throughout the AcuityMD platform using SQL. • Research issues end-to-end, identify root causes, and validate findings across multiple data sources. • Distinguish between expected platform behavior, customer education opportunities, and true product defects. • Communicate findings clearly to Customer Success, Support, and customers when appropriate. • Create detailed engineering tickets with reproduction steps, supporting evidence, and root-cause analysis. • Build deep expertise in AcuityMD's data model, business logic, and data pipelines. • Partner closely with Engineering, Product, Customer Success, and Support to improve platform quality and customer trust. • Identify recurring issues and opportunities to improve data quality, tooling, and operational processes. • Help Engineering validate fixes and ensure customer issues are fully resolved. • Expand your technical skills in Python, AI-assisted development, Git, and modern software engineering practices. • Learn how production software is designed, built, tested, and maintained by working alongside experienced software engineers. • Take on increasingly technical projects as your skills grow. • Build the technical foundation needed to transition into a Junior Software Engineer role through demonstrated performance and technical development.