• Lead the entire lifecycle for core product areas • Design, develop, and maintain critical backend services, automation tools, and Linux data collection software, prioritizing scalability, performance, and reliability • Contribute to the technical roadmap, proactively identifying future challenges and opportunities within Kubernetes security • Focus development efforts across the product on efficient data collection, event processing, storage, and persistence mechanisms • Spearhead the integration of new technologies, programming languages, and frameworks to boost developer productivity and drive product innovation • Uphold high standards for software quality, promoting a "design document-first" methodology and facilitating technical growth through code reviews and mentorship • Address complex customer issues, leveraging deep expertise in Kubernetes, Linux internals, and distributed systems to provide expert support • Collaborate cross-functionally with product managers, designers, and fellow engineers to refine and expand ACS features, maintaining a sharp focus on user experience and operational excellence • Provide technical guidance and foster peer learning through code reviews and design feedback to colleagues • Engage with the open-source community, sharing knowledge and contributing to projects foundational to ACS technologies.

• Design, build, and maintain data pipelines that power AI research and experimentation workflows • Develop backend services and APIs that give researchers clean, reliable access to data at scale • Work directly with researchers and AI builders to understand their data needs and translate them into robust engineering solutions • Improve data infrastructure reliability, observability, and performance • Debug and resolve data quality issues, identifying root causes and sharing learnings broadly • Contribute to internal tooling and paved roads that make it easier for the team to work with data consistently

• Analyze detailed technical specifications, documented requirements, and business rules to determine appropriate data solutions. • Design and maintain robust logical and physical data models that support enterprise analytics, reporting, and operational data needs. • Develop and maintain complex database objects including tables, views, stored procedures, and functions. • Design, develop, and maintain robust ETL pipelines to ingest, transform, and load data from multiple source systems. • Ensure data accuracy and consistency across systems by validating transformations and outputs against defined requirements. • Provide technical guidance related to database development, data modeling, and ETL design. • Support the promotion of database and ETL changes through development, testing, and production environments.

• Architect and develop backend microservices using Python (Flask/Django) and Java (Spring Boot). • Hands-on Experience in Laravel framework for building scalable, secure, and high-performance web applications • Implement dynamic frontends using js, react.js, or Vue.js, ensuring seamless API integration. • Integrate AI coding assistants like GitHub Copilot and Cursor AI for code generation, refactoring, and multi-file enhancement. • Optimize data strategies with relational (PostgreSQL/MySQL) and NoSQL (MongoDB) databases; familiarity with platforms like Snowflake or Databricks is a plus. • Mentor junior developers, lead code reviews, and drive architectural excellence. • Drive Agile ceremonies using Jira and Confluence, ensuring alignment between development and business goals