About the role
Works with appropriate internal stakeholders (e.g., product manager, privacy/security subject matter expert, technical lead) to understand and determine customer/user
requirements for a set of features. Incorporates customer insights into future designs or solution fixes with minimal supervision. Incorporates unwritten
requirements, such as appropriate continuous feedback loops that measure actionable, quantitative (e.g., customer value, usage patterns, solution performance) and qualitative (e.g., accessibility, globalization) indicators of value. Understands, and begins providing feedback on, and advocating for the security and privacy needs of the customer who will be using the set of features. Creates and implements code for a product, service, or feature, reusing code as applicable with minimal supervision. Writes and learns to create code that is extensible and maintainable. Considers diagnosability, reliability, and maintainability with few defects, and understands when the code is ready to be shared and delivered. Applies coding patterns and best practices to write code (e.g., leveraging state-of-the-art generative artificial intelligence [GenAI], approaches to source code organization, naming conventions). Escalates identified blockers or unknowns during the development process, communicates how they will impact timelines, and contributes to identifying strategies and/or opportunities to address them with minimal supervision. Builds knowledge, shares new ideas, and shares pinpoints of engineering tool gaps to improve software developer tools to support easier, faster, and more effective software engineering for complex product features. Identifies whether open source or internal code is available to address coding needs for a set of product features, and reuses it in a responsible manner where applicable. Develops higher-level awareness of tools outside current areas of expertise. Helps to identify and/or create tools that are useful for building the product, determining if methods are still applicable for the current solution. Uses appropriate artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC) in a disciplined manner. Takes responsibility for the content of their AI-generated changes to artifacts, reviewing all changes and applying appropriate tooling and processes with minimal guidance. Acts as a designated responsible individual (DRI), working on-call to monitor a system/product feature/service for degradation, downtime, or interruptions. Alerts stakeholders as to the status and gains approval to restore system/product/service for simple problems. Responds within service level agreement (SLA) timeframe. Escalates issues to appropriate owners. Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Master's Degree in Computer Science or related technical field AND 3+ 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 5+ 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
