Staff Engineer – Backend Engineering

WHO WE ARE:
Aviatrix® is the cloud network security company trusted by more than 500 of the world’s leading enterprises. As cloud infrastructures become more complex and costly, the Aviatrix Cloud Network Security platform gives companies back the power, control, security, and simplicity they need to modernize their cloud strategies. Aviatrix is the only secure networking solution built specifically for the cloud, that ensures companies are ready for AI and what’s next. Combined with the Aviatrix Certified Engineer (ACE) Program, the industry’s leading secure multicloud networking certification, Aviatrix unifies cloud, networking, and security teams and unlocks greater potential across any cloud.

About the Role - Staff Engineer – Backend Engineering

Aviatrix PaaS delivers intelligent, multi-cloud network and security services as a managed offering. We are seeking a Staff Engineer – Backend Engineering to lead the design and delivery of Golang-based microservices, large-scale data engineering pipelines, and enterprise SaaS platform operations on AWS. You will set technical direction, raise the engineering bar across the team, and drive platform reliability and scalability at enterprise scale.

Responsibilities

Architecture & Technical Leadership

• Own the end-to-end architecture and delivery of major backend platform capabilities; break down complex problems into well-scoped engineering efforts and guide execution.

• Define next-generation architectures for distributed Golang microservices and high-throughput data pipelines on AWS; establish standards the broader engineering org adopts.

• Write design proposals and technical documents that articulate trade-offs and long-term platform direction for engineering and product leadership.

• Drive improvements to platform reliability, security posture, and engineering quality across the team.

Golang Microservices Engineering

• Lead design and implementation of production-grade Golang microservices; establish team-wide service patterns, API conventions, and coding standards.

• Architect synchronous (gRPC, REST) and asynchronous (event-driven) inter-service communication with a focus on fault tolerance and operational simplicity.

• Drive SLO/SLA definition, distributed tracing, structured logging, alerting, and capacity planning across the microservices estate.

• Set a high bar through rigorous design and code reviews; mentor engineers on idiomatic Golang, testability, and production readiness.

Data Engineering & Pipelines

• Architect and deliver large-scale data engineering pipelines for real-time streaming and batch workloads, with a focus on reliability, throughput, and data quality.

• Own adoption and integration of AWS data pipeline services: Amazon Kinesis, Amazon MSK (Kafka), AWS Glue, AWS Step Functions, and Amazon EMR.

• Define pipeline reliability standards: idempotency, dead-letter queue handling, delivery semantics, schema governance, and back-pressure management.

• Establish pipeline observability practices: throughput/latency dashboards, data quality monitoring, lineage tracking, SLA alerting, and cost-per-record analysis.

SaaS Platform Operations

• Drive SaaS-specific platform capabilities: multi-tenancy isolation, tenant-aware data partitioning, rate limiting, quota management, and resource fairness.

• Enforce AWS networking and security best practices: VPC architecture, IAM least-privilege, PrivateLink, Transit Gateway, and encryption in transit and at rest.

• Lead platform infrastructure-as-code practices (Terraform/CDK) and CI/CD pipeline standards for consistent, auditable deployments.

• Own the operational model: runbook standards, on-call practices, incident management, and post-incident review culture.

Team Leadership & Influence

• Mentor engineers and serve as a technical anchor for the team; foster a culture of rigorous technical thinking and continuous improvement.

• Partner with product management and leadership to align backend platform investments with business priorities and customer commitments.

• Lead escalations and critical production incidents; drive root-cause analysis and long-term remediation.

• Contribute to recruiting and interviewing; help define bar-raising standards for backend and data engineering hires.