Senior / Lead Backend Engineer (IGT1)
About the role:
We’re looking for a Lead / Senior Software Engineer to help build and operate high-impact backend systems that power intelligent, data-driven digital experiences at scale. The team simplifies complex platform capabilities into reliable, reusable services in a cloud-native, multi-tenant environment.
Sitecore operates in an AI-driven engineering environment and expect you to use copilots and agentic workflows to accelerate delivery, while keeping the bar high for correctness, security, and maintainability. This is not “vibe coding.” We want someone who can ship without AI and also knows how to steer it effectively.
You’ll have real influence over architecture decisions and platform direction, not just execute on tickets. You’ll work in a team that’s genuinely investing in AI-assisted engineering, not just talking about it. We value open debate, evidence-based decisions, and a collaborative environment where ideas are challenged constructively.
Key Responsibilities:
- Solve problems autonomously: proactively reach out to other teams, stakeholders, and subject-matter experts to gather information and unblock yourself.
- Collaborate with product owners and cross-functional teams to define requirements, influence platform direction, and architect solutions to complex challenges.
- Contribute to architectural discussions: evaluate design patterns, propose solutions, constructively challenge ideas, and help the team find the best fit following established best practices.
- Design, build, and maintain Go services in a cloud-native, multi-tenant environment.
- Build shared libraries, SDKs, and internal tooling that make it easier for other teams to integrate with and operate platform services.
- Evolve and optimize our cloud-based platform, balancing relevance, latency, resilience, and cost.
- Build resilient, observable systems with proper timeouts, retries/backoff, caching, rate limiting, SLOs, and safe rollout patterns.
- Lead code reviews, provide technical mentorship, and raise the team’s engineering bar.
- Leverage AI tools (copilots, agents) to accelerate coding, testing, and debugging with strong verification habits, and help shape team-level best practices for AI-assisted workflows.
Qualifications
Preferred Skills and Experience:
Core Engineering
- 5+ years of professional software engineering experience (or equivalent senior-level impact).
- Strong backend engineering experience in Java, .NET, or similar languages, with a willingness to learn and ramp up in Go after joining.
- Experience building and running distributed systems in production (microservices, event-driven architectures, failure modes).
- Solid cloud-native experience (Azure, or another major cloud provider) with Kubernetes, identity/secrets management, networking, and CI/CD.
- Experience with infrastructure as code (Terraform, Pulumi, or similar).
- Strong debugging and incident skills: can diagnose issues from logs, metrics, and traces end-to-end.
AI-Native Engineering
- Comfortable using LLM tools and agents in daily work (scaffolding, refactoring, test generation) without sacrificing quality.
- Strong verification mindset: treats AI output as untrusted until validated via tests, reviews, benchmarks, and security checks.
- Skilled at context engineering: can package high-signal context to reliably steer models.
- Interest in emerging approaches (agents, MCP-style tool use, structured workflows) and ability to evaluate them pragmatically.
Additional Skills That Could Set You Apart
- Experience with Go
- Hands-on experience with search engines (Elasticsearch, Solr, Lucene, Azure AI Search, or similar) including index design and relevance tuning.
- Experience with vector search, embeddings, or semantic retrieval techniques.
- Experience with distributed NoSQL databases (Cosmos DB or similar) including partitioning and throughput tuning.
- Observability depth: OpenTelemetry, Prometheus/Grafana, or Application Insights.
- Experience with messaging/streaming systems (Kafka, Azure Service Bus, Event Hubs, or similar).
- Security and AI safety awareness: prompt injection risks, safe tool outputs, least-privilege automation.
What Success Looks Like
- You ship reliable features to production with strong test coverage and clear operational signals.
- You proactively improve performance, reduce cost, and increase resilience across the platform.
- You raise the team’s bar on AI usage: faster delivery and fewer regressions through better workflows and verification.
- You mentor others and improve the system, not just the code.
Additional Information
We champion flexibility and hybrid work options to support varying lifestyles and personal needs. At the same time, we value the power of in-person collaboration to build community, spark innovation, and strengthen connections. Our approach ensures you can work in ways that suit you best while still engaging with colleagues to share ideas and grow together. #LI-Hybrid #LI-DNP