Staff Software Engineer – Search Data Infrastructure

Salesforce

Staff Software Engineer working on Slack's search data infrastructure. Design and develop scalable solutions while collaborating across teams to enhance user experience at scale.

Posted 5/15/2026full-timeSeattle • Texas, Washington • 🇺🇸 United StatesLead💰 $172,500 - $313,700 per yearWebsite

Tech Stack

Tools & technologies

ChefElasticSearchGoHadoopJavaKubernetesMapReducePHPPythonRubySparkTerraform

About the role

Key responsibilities & impact

Design and develop scalable, resilient offline indexing pipelines that process and transform data for Slack's search infrastructure.
Partner with product engineering to conceptualize and ship new features for our large — and rapidly growing — user base.
Ensure high availability and reliability across our distributed search systems through thoughtful collaboration and rigorous engineering.
Drive significant business impact by contributing heavily to high-priority team projects.
Take long-term ownership of team features and systems, proactively improving their health and the health of surrounding services.
Deploy, configure, test, troubleshoot, maintain, and upgrade Solr clusters and environments.
Continuously optimize Solr cluster infrastructure and configuration to sustain cluster health and search performance.
Support our operations and customer-facing teams in triaging and resolving production issues efficiently.
Conduct thorough, collaborative code reviews that raise the quality bar across the team.
Champion improvements to engineering standards, tooling, and development processes.
Build and ship production-grade software using modern engineering practices, with AI as a core pillar of your development workflow — leveraging AI tools to deliver secure, optimized, and high-quality code at speed.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining a shared system context — an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human- or AI-generated) for correctness, quality, security, and performance.

Requirements

What you’ll need

10+ years of hands-on experience with distributed data and search technologies.
Have worked extensively with distributed data processing frameworks such as Spark, Hadoop, MapReduce, or EMR.
Experience with Solr, Elasticsearch, Lucene, or comparable search technologies.
Are familiar with infrastructure and ops tooling such as Terraform, Chef, and Kubernetes.
Proficiency in functional or imperative programming languages — e.g., PHP, Python, Ruby, Go, C, or Java.
Track record of building high-availability, performant, and scalable systems that handle large volumes of data across multiple clusters in geographically distributed environments.
Experience planning, implementing, and executing software migrations and upgrades in production environments.
Confidence in contributing to technical architecture discussions and influencing technical decisions within your team.
Bring a genuine, demonstrated AI-first approach to engineering — using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Have experience integrating AI development tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) into day-to-day engineering workflows.
Possess advanced prompt engineering skills — writing precise, structured prompts and cultivating system context that makes AI outputs reliable, secure, and production-ready.

Benefits

Comp & perks

time off programs
medical
dental
vision
mental health support
paid parental leave
life and disability insurance
401(k)
employee stock purchasing program

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

distributed data processingSolrElasticsearchSparkHadoopMapReduceAI development toolsprompt engineeringprogramming languagessoftware migrations

Soft Skills

collaborationcode reviewtechnical architecture discussionsinfluencing technical decisionsownershipproblem-solvinginnovationcommunicationcritical evaluationengineering standards