Partners with software development teams to understand how they evaluate and improve productivity. Uses this insight to design new metrics and measurement approaches for EngThrive Insights. Builds extensible, reliable, and maintainable artifacts (data pipelines, schemas, dashboards, etc.) using AI-first tools and engineering practices. Assesses delivery readiness, minimizes defects, and flags blockers early with clear timeline impacts and mitigation options. Identifies gaps in our technology stack, then helps close them with practical solutions. Evaluates and responsibly reuses internal or open-source code when appropriate, while building awareness of tools beyond the immediate domain. Uses AI tools across the software development lifecycle in a disciplined manner. Reviews every AI-generated change, applies appropriate validation and engineering processes, and remains accountable for the final result. Translates user satisfaction into measurable feedback loops and secure, privacy-aware metrics. Incorporates quantitative signals such as usage and performance, along with qualitative needs such as accessibility and globalization. Bachelor's Degree in Computer Science or related technical field AND 86+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience with dimensional modeling and analytics engineering (e.g., star schemas, medallion architecture, semantic layers). Experience with analytical query languages like SQL, KQL (Kusto Query Language) and Azure Data Explorer, or similar analytical query languages. Experience developing applications on Spark-based big data platform technologies such as Databricks. Experience building REST APIs or MCP servers with web application frameworks. Experience building or integrating with LLM-powered tools, AI agents, or MCP (Model Context Protocol) servers. Experience with DevOps, CI/CD pipelines, infrastructure-as-code, and production monitoring/observability. Quantitative aptitude and ability to reason about metric definitions, data quality, and statistical validity. Familiarity with developer productivity metrics and frameworks (e.g., DORA, SPACE, or similar). Experience collaborating with data & applied scientists Demonstrated effective use of AI coding assistants in daily development workflow