• Design, implement, scale, and maintain backend systems that process large volumes of data • Work on event-driven and API-based integrations • Contribute to migration efforts toward a more domain-based and scalable architecture • Build and optimize software for performance-sensitive workflows • Investigate technical problems and propose solutions together with the team • Collaborate closely with data science and engineering colleagues on technical solutions • Participate in a collaborative development model where work is shared rather than handled alone

• Design, implement, scale, and maintain backend systems that process large volumes of data. • Work on event-driven and API-based integrations. • Contribute to migration efforts toward a more domain-based and scalable architecture. • Build and optimize software for performance-sensitive workflows. • Investigate technical problems and propose solutions together with the team. • Collaborate closely with data science and engineering colleagues on technical solutions. • Participate in a collaborative development model where work is shared rather than handled alone.

• Design, implement, scale, and maintain backend systems that process large volumes of data. • Work on event-driven and API-based integrations. • Contribute to migration efforts toward a more domain-based and scalable architecture. • Build and optimize software for performance-sensitive workflows. • Investigate technical problems and propose solutions together with the team. • Collaborate closely with data science and engineering colleagues on technical solutions. • Participate in a collaborative development model where work is shared rather than handled alone.

• Define and drive the long-term engineering vision, strategy, and roadmap • Partner with executive leadership (CTO, Product, Sales and Sales Ops Leaders) to align technology initiatives with business objectives • Evaluate emerging technologies (Generative AI, Agentic AI, LLMs, MCP, automation platforms) and translate them into measurable business value • Lead technology transformation, modernization, and innovation efforts • Oversee end-to-end software development lifecycle (SDLC) including architecture, development, testing, release, and operations • Ensure delivery of high-quality, scalable, secure, and reliable software systems • Establish and monitor engineering KPIs (velocity, quality, uptime, cost efficiency) • Establish an AI-first software engineering framework leveraging GenAI-assisted development, intelligent testing, code generation, documentation, and deployment automation to significantly improve developer productivity and software quality • Drive architectural decisions, platform strategy, and technology standards • Ensure modern engineering practices (cloud-native, microservices, DevOps, CI/CD, AI integration where relevant) • Balance build vs. buy decisions and optimize technology investments • Work closely with Product Management to deliver customer-centric solutions • Partner with sales and sales ops team to listen to voice of customer and improve customer satisfaction • Partner with Security, Infrastructure, Data, and Operations to ensure integrated delivery and governance for responsible AI • Align with finance and business leaders on budgeting and ROI for investment • Establish governance, processes, and best practices for engineering operations • Improve engineering productivity, quality, and predictability • Manage budgets, vendor relationships, and external partners • Build, lead, and scale high-performing engineering teams across multiple disciplines while fostering an AI-first culture emphasizing continuous learning and upskilling • Recruit, mentor, and retain top engineering talent and while ensuring we have succession plans & leadership pipelines