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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Engineering - Data Infrastructure & Reliability - **Company:** CrowdStrike - **Location:** Austin, TX, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Data Infrastructure, Distributed Data Store, Distributed Systems, Python (Programming Language), Online Analytical Processing, Reliability Engineering, Ansible, Prometheus, Software Engineering, Test Data, Pulumi, Large Language Models, Grafana, Apache Spark, Kubernetes, Information Technology, Apache Flink, Cassandra, Apache Kafka, Search Engines, Terraform, Dynatrace, Golang - **Published:** September 28, 2026 - **Apply:** https://crowdstrike.wd5.myworkdayjobs.com/crowdstrikecareers/job/USA---New-York-NY/Director--Engineering---Data-Infrastructure---Reliability_R30161 ## About the Role problems. We're always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you., * 12+ years of software engineering experience, including 5+ years leading engineering teams and at least 2 years leading through other managers. * A track record of building, growing, and retaining high-performing platform, SRE, or infrastructure teams in a fast-paced, high-growth environment, including hiring senior engineers onto a team that did not exist yet. * Hands-on grounding in SRE practice at scale: SLOs, SLIs, error budgets, incident command, blameless postmortems, and capacity planning for high-throughput distributed systems. * Experience owning reliability for large-scale stateful distributed systems, where ordering, data state, and recovery semantics rule out generic automation. This is the hardest technical part of the job. * Working fluency with distributed data infrastructure: streaming platforms, OLAP and search engines, object storage, and large-scale query systems. Enough to hold a credible design conversation and to recognize a bad proposal. * Ownership of a substantial infrastructure cost portfolio, including driving optimization across organizational boundaries and shifting cost accountability to the teams that control the spend. * Proven ability to influence without authority. You have changed how engineering teams outside your reporting line operate, and you can explain how you earned that adoption instead of mandating it. * Experience operating under regulatory, residency, or certification constraints, and comfort turning control requirements into engineering work. * Strong executive communication. You can compress a complicated reliability or cost story into something a leadership team can decide on, and you do not let your team's work go unseen. * A specific point of view on applying AI to reliability and operations: where it pays off, where it does not, and what you would build first. * Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes. * Bachelor's degree in Computer Science or related field, or equivalent work experience. Bonus Points: * Experience standing up sovereign, air-gapped, or regulated cloud environments, and automating the evidence that proves they comply. * Hands-on depth with Kafka, Flink, Spark, Cassandra, OpenSearch, Pinot, Trino, or comparable systems at petabyte scale. * Python and/or Golang, Infrastructure as Code (Terraform, Ansible, Pulumi), Kubernetes at fleet scale, and GitOps workflows. * Advanced observability practice with Prometheus, Grafana, OpenTelemetry, distributed tracing, and large-scale log aggregation, weighted toward SLO dashboards and reliability scorecards rather than vanity metrics. * Having built and run a chaos engineering or game day practice that other teams joined voluntarily. * FinOp, or having led a cost optimization and/or re-attribution program to completion. * Having shipped LLM-native or agentic tooling for incident prevention, triage, or remediation. ## Description As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn't changed - we're here to stop breaches, and we've redefined modern security with the world's most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex, * Stand up the team: define its operating model, hire net-new engineers, and bring in senior operators from existing data platform teams without leaving holes behind them. * Hire, develop, and lead engineering managers and technical leads as the team grows from one group into several domain-aligned ones. * Set the technical bar. Your team's output is tooling, harnesses, and guardrails that engineers outside your org have to actually want to adopt. * Own org design as the charter matures: forming, rechartering, and sequencing teams against where the platform's risk actually sits. Cloud Expansion Automation * Build the automation that makes stateful data platform systems first-class citizens in our cloud build tooling, including the ordering, state awareness, and dependency sequencing that generic SRE automation cannot handle. * Take regional and sovereign cloud build-outs to full automation, so standing up a new environment is a repeatable, hands-off sequence. * Own time-to-launch per cloud and region as a headline metric, alongside the teams that own the destination architecture. End-to-End Validation & Continuous Health Checks * Build a testing system that generates and traces data from the CrowdStrike sensor through ingestion to query validation. The same harness does pre-launch validation for every new cloud and region, then continuous health checks in steady state. * Raise the fidelity of generated test data so it exercises the same paths production traffic takes, and a passing run means what it claims. * Hold end-to-end (i..e sensor to query) coverage and escaped-defect rate as first-class metrics for every launch. Observability & Proactive Incident Response * Standardize reliability and efficiency metrics across every data platform team, so the whole platform reports from one instrumented view. * Build detection that catches problems before customers report them, with automated routing to the right responders plus first-line triage and guidance for owning teams. * Set and drive down targets for mean time to detect, respond, and recover, backed by instrumentation that proves the trend. * Push the incident rate between releases and operations down by addressing systemic causes. Cost Engineering * Run fast detection and remediation for cost anomalies, so an efficiency regression surfaces in days rather than quarters. * Find and execute optimization work across the ecosystem, either directly with partner teams or by handing them tooling. Some of it is straightforward compute migration. Some of it is pipeline and architecture rework. * Move cost accountability to the layers that own the levers, so that application teams see what their query patterns, storage choices, and pipeline hops actually cost. This is as much an organizational change as a technical one, and you will lead it. Resilience, Scale & Capacity Planning * Establish a recurring game day practice that stresses systems deliberately to establish their real limits. * Run scale and stress testing ahead of projected demand. * Work with data platform teams on forward-looking capacity planning, and hold forecast accuracy as a real metric. Data Residency & Compliance Automation * Turn residency verification and audit evidence into reusable platform capabilities that every new region inherits. GRC defines the controls and other teams own the sovereign architecture; your team automates the data platform's side of proving both. * Build automated validation that data lands, is processed, and is queried inside its declared residency boundary, wired into the same harness as the rest of the testing rather than a parallel one. * Express the data platform's share of sovereign and certification controls as executable checks that run continuously, and generate attestation evidence from telemetry so certification and re-certification draw on evidence that is always current. * Treat a control that has quietly stopped holding as an incident, detected and routed under the same targets as any reliability event. Executive Narrative & Cross-Functional Influence * Turn your team's delivery into a clear executive story on reliability, cost, and compliance posture, and carry it into senior forums with candor about risk. * Earn adoption from engineering leaders who do not report to you. A horizontal team runs on that trust. * Negotiate scope deliberately. 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