Staff Software Engineer

The Role 

Proofpoint’s Archiving Ingestion team is looking for a Staff Software Engineer to work from the Toronto office. 

Proofpoint's Archive is the backbone of our Digital Communications Governance (DCG) product line, serving some of the world's largest enterprises with mission-critical data ingestion, storage, and retrieval. The Ingestion team owns the systems that bring data into the Archive at scale, processing millions of messages daily across email, collaboration, and cloud connectors. 

We are hiring a Staff Software Engineer to serve as the technical lead for ingestion and data engineering across DCG. This is a high-leverage role: you will define how data enters the platform, at what cost, at what reliability, and with what guarantees. As we evolve the ingestion pipeline to handle additional data types, the architectural decisions made in this role will shape the platform for years. You will be based in Toronto and be the technical lead of a hybrid development team spanning Toronto and India. 

What You Will Own 

• Architectural direction of the ingestion platform, including Kafka topology, EKS deployment model, as well as feature and API design. Drive the roadmap to a scalable, robust ingestion platform that can handle tomorrow’s needs. 

• Engineering standards for the team: code quality, observability, and incident response. 

• Ingestion SLOs: define and own SLOs for throughput, latency, data quality, etc. Build observability stacks to track them in production. 

• Incident response model for ingestion: lead RCAs, define failure mode mitigations, and eliminate classes of incidents through systemic fixes. 

• Cost-aware platform improvements that reduce cloud cost-to-serve while preserving reliability, performance, and customer experience. 

• Technical leadership and mentoring of a distributed team of engineers (Toronto & India), fostering engineering excellence and accountability across geographies. 

Your Day-to-Day 

• Partner with product and engineering leadership to shape the team’s roadmap, making scope and trade-off decisions grounded in operational and cost realities. Help define or refine requirements of engineering-led features. 

• Drive architecture reviews, design doc culture, and engineering standards that apply across the MIP team and influence adjacent platform teams. 

• Lead processor engineering on EKS: pod lifecycle management, KEDA-driven autoscaling, resource optimization, and deployment safety for high-throughput workloads. 

• Leverage AI tools (Claude Code, Copilot, etc.) to accelerate design, review, and implementation cycles where they add genuine benefit. Features can extend across software, infrastructure-as-code, automation, and cloud services to enhance the data ingestion capabilities of the Archive. 

• Cross-team influence: aligning other feature teams on ingestion interfaces, APIs, and contracts. 

• Help to troubleshoot and resolve difficult production problems that can affect our various customers across multiple regions and accounts. 

• Be available for very limited on-call during critical deployment activities. 

• Mentor engineers at all levels, fostering technical excellence, knowledge sharing, a culture of innovation, and engineering coherence across timezones. 

What You Bring to the Team (Required) 

• Data engineering principles at enterprise scale: idempotency, deduplication, data quality validation, and observability. 

• Deep expertise in streaming and event-driven systems: Kafka (or MSK/Confluent) consumer group design, partition strategy, offset management, schema evolution, and exactly-once semantics. 

• AI-first mindset: You excel with spec-driven development and are willing to mentor others to do the same. 

• Backend systems development: Strong Java engineering; REST API design; database programming; experience with distributed systems design, RPC, service discovery, and advanced OO concepts. 

• Kubernetes / EKS operational depth: pod specs, resource quotas, autoscaling (KEDA), Helm chart management, deployment strategy for stateful or high-throughput workloads.  

• Infrastructure-as-Code proficiency: Terraform/Terragrunt for managing ingestion infrastructure reproducibly. 

• Cloud infrastructure expertise: AWS services including MSK, EKS, S3, IAM, Lambda, and CodePipeline; experience reasoning about cost, multi-AZ resilience, and operational observability in AWS environments.  

• Cost-aware mindset: You think about throughput-per-dollar, right-sizing, and the cost implications of architectural choices as a matter of habit. 

• Demonstrated Staff-level technical leadership: architecture ownership, cross-team influence, mentorship, and setting high standards of engineering quality, in a distributed team context. 

• Communication and collaboration: Excellent verbal and written communication, to drive technical alignment across engineering, product, and security.  

• Education: Degree in Computer Science, Computer Engineering, or equivalent. 

What Sets You Apart (Strongly Preferred) 

This role is a blend of AI-first software engineering and modern DevOps approaches. While strong AI-driven development skills are a hard requirement, any extra infrastructure, cloud service, automation, and configuration management skills are a plus. 

Some of these nice-to-haves include: 

• Familiarity with archiving, eDiscovery, compliance, storage, or email transport (SMTP/IMAP/Exchange) domains. 

• Experience designing connector protocols or multi-tenant ingestion systems at scale. 

• Experience with these technologies: RDS/MySQL, SQLite, mail transport agents, Argo Workflows, GitOps 

• Experience with Agile/Scrum in a distributed team; opinions on how to run engineering ceremonies effectively across time zones. 

• Experience with stream processing frameworks: Kafka Streams, Apache Flink, or Spark Structured Streaming. 

Why This Role 

This is a greenfield technical leadership opportunity on a platform that matters. You will have genuine ownership over a critical system that underpins one of Proofpoint's core product lines. If you care deeply about data engineering craft, operational excellence, and building systems that scale without breaking the bank, this is the role.