Lead Software Engineer - DevOps & Infrastructure, Employee Platforms

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience in system design, application development, testing, and operational stability.
• Hands-on experience designing, developing, and operating ETL/ELT data pipelines using Python, SQL, and distributed data-processing technologies.
• Strong understanding of data warehousing concepts, including dimensional modeling, star and snowflake schemas, slowly changing dimensions, partitioning, and query optimization.
• Experience developing production data solutions with Databricks, Apache Spark or PySpark, Delta Lake, Lakeflow pipelines, Lakeflow Jobs, and lakehouse architecture.
• Knowledge of data governance, lineage, access controls, schema management, data quality, and production monitoring.
• Experience integrating data from relational databases, APIs, files, message streams, and cloud-based storage platforms.
• Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database query languages.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
• Strong knowledge of the Software Development Life Cycle (SDLC), Agile delivery methodologies, CI/CD practices, application resiliency, and secure software development principles.

Preferred qualifications, capabilities, and skills

• Experience with AI-powered coding assistants such as GitHub Copilot and Claude Code.
• Familiarity with prompt engineering, embeddings, and RAG pipelines.
• Experience building operational copilots or chatbots for runbooks or troubleshooting.
• Proficiency in Python (Go is a plus).
• Experience with Databricks Unity Catalog, Structured Streaming, data governance, and performance optimization.
• Familiarity with cloud data platforms and infrastructure-as-code or deployment automation for data workloads.
• Databricks certification or equivalent experience delivering production-grade lakehouse solutions.