Principal Engineer - Revenue Management & Intelligence

Salesloft Salesloft · Enterprise · United States · Engineering

Principal Engineer role focused on building AI-driven enterprise-grade applications for revenue management and intelligence. The role involves shaping interfaces, leading cross-functional projects, debugging production issues, owning architecture decisions, and mentoring engineers. Requires expertise in distributed systems, cloud infrastructure, data infrastructure, SQL/NoSQL, and platform monitoring.

What you'd actually do

  1. Shape future-proof interfaces that are easy to build against and meet the requirements of client-facing teams
  2. Brainstorm with Product Managers, Designers and Engineers, and other cross-functional partners to conceptualize and build new features for our growing user base
  3. Produce high-quality results by leading large cross-functional projects that have a significant impact on the business
  4. Debug production issues across services and multiple levels of the stack, and uplevel the observability and reliability of the overall system
  5. Own architecture decisions across multiple teams, drive long term technical strategy, and raise the bar for engineering excellence company-wide
  6. Mentor Staff and Senior engineers

Skills

Required

  • 12+ years of experience in building distributed systems or platform infrastructure designed for high-scale, data-intensive workloads
  • Expert-level proficiency in Java or a similar object-oriented language
  • familiarity with infrastructure tooling (Airflow, Kubernetes, Ray, Terraform)
  • familiarity with service frameworks (FastAPI, gRPC)
  • familiarity with observability platforms (Prometheus, Grafana, DataDog, Splunk)
  • Deep experience with cloud infrastructure, ideally AWS or GCP
  • Architected solutions requiring distributed data infrastructure: message queues (Kafka, Kinesis), caching systems (Redis, Memcached), database optimization, and building resilient, fault-tolerant architectures
  • Expertise with SQL (postgres) / NoSQL (MongoDB) and data warehouse modeling
  • capable of optimizing complex data queries, joins, aggregations, and transformations
  • Solid improvements of platform monitoring and optimizations, including identifying performance bottlenecks, latency optimization, cost management, and scaling high-throughput API services efficiently

Nice to have

  • Kubernetes clusters (GKE/EKS)
  • serverless architectures
  • managed services (e.g., Lambda, Cloud Run, ECS)

What the JD emphasized

  • AI-driven enterprise-grade applications
  • leading-edge cloud-based technologies
  • high-scale, data-intensive workloads
  • data-intensive workloads

Other signals

  • AI-driven enterprise-grade applications
  • Predictive Revenue System
  • Revenue Context