Senior Engineering Manager (L5)

As a Senior Engineering Manager on the Twilio Segment Data platform/pipelines team, you'll build and scale systems that process several hundred thousands of data points per second.

You'll lead the development of high-scale ingestion and data processing systems, guiding the team in designing, operating, and maintaining complex distributed systems, ensuring reliability, performance, and cost-efficiency while querying petabytes of data for our customer data platform (CDP).

Responsibilities:

• Own and deliver robust, high-scale routing experiences for Data platforms & pipelines for Twilio Segment.
• Champion team growth and success, prioritizing mentorship and individual development.
• Architect and operate always-available, complex distributed systems in cloud environments.
• Guide technical decisions, articulating trade-offs between cost, performance, and reliability.
• Advocate for agile processes, continuous integration, and robust automation.
• Translate complex technical strategies into actionable plans for diverse stakeholders, including Product Managers and Architects.

Qualifications:

• 14+ years of engineering experience with a 5+ year proven track record managing software teams.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Proven experience managing multiple workstreams within the team.
• Demonstrated commitment to driving an AI-centric engineering mindset and providing mentorship on leveraging AI-enabled development platforms.
• Technical depth in distributed systems, high-scale ingestion, and cloud-native architecture.
• Hands-on experience with high-scale messaging/streaming systems and processing engines (several thousand events/sec).
• Excellent communication, problem-solving, and operational excellence skills.

Desired:

• Demonstrated experience identifying, prototyping, and deploying GenAI/LLM solutions to solve real-world engineering or data processing challenges.
• Expert programming experience in Go or similar object-oriented languages.
• Proficiency with datastores, distributed caching, and container technologies (Docker/Kubernetes).
• Experience with modern CI/CD pipelines and observability tools (Datadog, Grafana, Prometheus).
• Familiarity with AI/ML technologies.
• Experience managing and operating AWS infrastructure.
• Experience working in a geographically distributed team.