Director, Engineering - Data Infrastructure & Reliability

CrowdStrike•New York City, NY

1d•Hybrid

About The Position

CrowdStrike's Data Platform is the foundation beneath Falcon and Next-Gen SIEM: the ingestion, streaming, storage, and query systems that take in almost 3 trillion events per day, at millions of events per second, and make them searchable in seconds. It spans multiple clouds and regions, including sovereign and regulated environments, and it holds petabytes of security telemetry that customers rely on in the middle of active investigations. We set a high bar for that platform. Reliability, cost efficiency, and provable compliance are engineering requirements here rather than afterthoughts, and we are raising that bar again as we move the organization toward domain ownership, data as a product, self-serve tooling, and federated governance. We support planetary scale and our systems are stateful and order-dependent, which puts them outside what generic cloud build automation is designed for. Validating a change means tracing real data from the sensor through ingestion to query rather than exercising a service in isolation. The largest cost levers are spread across every team that queries or stores data. Each new sovereign region has to prove residency and controls on its own evidence. Work like this is worth far more solved once, well, and for everyone than solved separately on six teams. We are forming a centralized Data Platform Enablement team to own that category of work, and we are hiring its founding engineering leader. You will start by hiring and forming a small, senior team, then grow it into a multi-team organization as the charter expands. The team works as a horizontal function outside individual product team scope, in three modes. It builds platform-wide automation, tooling, and test infrastructure. It partners hands-on with individual data platform teams. And it enables the rest of the organization through guardrails, best practices, and self-service tooling. Every hour your team spends on cross-cutting reliability, testing, and cost work is an hour another data platform team gets back for work in its own domain. The reach of this role is wider than its headcount. You will not own the product domains, but you will answer for reliability, cost, and compliance outcomes across all of them, which means most of your influence has to come from somewhere other than your reporting line. This one suits someone who would rather build a function than inherit one. This role is hybrid, linked to one of the posted locations: Austin, TX; Sunnyvale, CA; Redmond, WA; or NY, NY.

Requirements

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.
Working fluency with distributed data infrastructure: streaming platforms, OLAP and search engines, object storage, and large-scale query systems.
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.
Experience operating under regulatory, residency, or certification constraints, and comfort turning control requirements into engineering work.
Strong executive communication.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.
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.
Turn residency verification and audit evidence into reusable platform capabilities that every new region inherits.
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.
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.
Negotiate scope deliberately.

Benefits

Market leader in compensation and equity awards
Comprehensive physical and mental wellness programs
Competitive vacation and holidays for recharge
Paid parental and adoption leaves
Professional development opportunities for all employees regardless of level or role
Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
Vibrant office culture with world class amenities
Great Place to Work Certified™ across the globe
health insurance
401k
paid time off