Sr Manager, Software Development & Engineering Lead (PL)

Charles SchwabAustin, TX
4dOnsite

About The Position

Your Opportunity At Schwab, you're empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s). Schwab's AI Engineering & Operations team is responsible for building the next generation Generative AI solutions that shape the future of technology at Schwab. Engineers collaborate across teams to deliver scalable, secure, and high-performing AI systems that align with Schwab's innovation strategy and operational goals. Additionally, the AI products this team delivers are instrumental in driving data-informed business decisions and elevating client experiences. We're seeking a Senior Manager to lead a team of engineers who will design and deliver production-grade GenAI applications, enhancing the client journey and driving tangible business value. In this pivotal role, you will help the team advance technical standards and solve complex challenges and lead rapid iterations from concept to production. You will bring curiosity, creativity, and technical depth to help shape the next era of AI at Schwab, with a special emphasis on site reliability, monitoring, observability, and operations. You'll ensure that the systems the team builds are robust, reliable, and well-monitored, implementing best practices for observability and operational excellence to maintain high performance and uptime for mission-critical AI applications.

Requirements

  • 5+ years direct people leadership experience with a proven ability to cultivate talent and build a collaborative team environment, preferably in a large, complex, and geographically dispersed organization
  • 8+ years of software development experience, with 4+ years as a hands-on senior engineer
  • Bachelor's degree in Computer Science or related field.
  • 5+ years building complex products from scratch, nurturing them in production, and ensuring operational reliability.
  • 3+ years developing applications and data pipelines interfacing with large datasets.
  • 5+ years working with containers and cloud-native applications, operationalizing them in the public cloud with infrastructure as code.
  • Demonstrated ability to influence and execute through others, deliver balanced and actionable feedback, and manage multiple priorities.
  • Strong people management skills, including hiring, mentoring, performance management and career development.

Nice To Haves

  • Strong computer science fundamentals and experience across the tech stack.
  • Commitment to quality—driving high standards including writing tests at all levels.
  • Strong written and verbal communication skills to clearly convey ideas and feedback.
  • Strong emotional intelligence, maturity, and executive presence, inspiring confidence and creating followership at all organizational levels.
  • Mentoring junior engineers and supporting their technical growth through code reviews and guidance.
  • Highly detail-oriented and process-focused with a mindset of continuous learning and improvement.
  • Ability to solve complex problems with ambiguous or incomplete data in distributed systems.
  • Demonstrated business domain knowledge relevant to previous products.
  • Curiosity about new technologies and processes, proactively sharing knowledge and seeking improvement.
  • Experience with Python, Java, and front-end development preferred but not required.
  • Master's or advanced degree in Computer Science or related field.

Responsibilities

  • Provide leadership by mentoring and coaching engineers, fostering strong practices, accountability, and continuous learning.
  • Lead the team in solving complex technical challenges and driving rapid iteration from concept to deployment.
  • Champion reliability, monitoring, observability, and operational best practices for AI systems and data pipelines.
  • Implement and maintain monitoring, alerting, and incident response frameworks to ensure system health and reliability.
  • Collaborate with cross-functional teams to align solutions with enterprise strategy and technical standards.
  • Advance engineering standards, focusing on operational excellence and quality across all deliverables.
  • Exhibit strategic vision, initiative, and adaptability while navigating complex situations and driving organizational alignment.
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