Sr. Manager, Engineering - Data Analytics Infrastructure (Hybrid)

CrowdStrike is seeking a Sr Engineering Manager (L8) to lead a host of initiatives spanning 2-3 teams with coverage in MLOps & broader data infrastructure, as well as data analytics platform & data recovery initiatives.

Your teams would own data plane infrastructure that runs a multitude of Spark-, Flink- and other apache-based applications at incredibly high scale (over 3 Trillion events/day) both in our cloud environments as well as data centers. You own the setup, deployment, scaling and management of all data plane products that are used to ingest, process and enrich event stream data that are part of the Falcon platform.

In addition, you will continue to build out & scale teams capable of supporting the myriads of services running within the our data infrastructure. You will work closely with engineers and product to understand requirements and cross functionally with peers and senior leadership to deliver innovative products that can scale with increased demand. CrowdStrike is a remote-first organization and this position requires the ability to hold meetings with team members across US and EU time zones. 

This is a Hybrid role requiring an 2 days per week in our Austin, TX office.

What You'll Do:

• Lead multiple teams in the design, scaling and maintenance of data infrastructure services operating at 100s of PBs scale

• Train, mentor and continue to hire top-tier data platform engineering talent

What You'll Need:

• MS in Computer Science or related field

• 10+ years of software engineering experience in building and/or managing large scale data platforms

• 5+ years of hands-on management experience leading distributed engineering teams

• Experience building and supporting real time or NRT data systems of ultra high scale (MINIMUM: 100s of TB/day in either a current or past role)

• Significant expertise in performance-tuning or developing internals (source code) within Spark, Flink, Iceberg or Pinot or an equivalent structured streaming, Time Series, OLAP or Open Table real time system.

• Production experience building either Spark- or Flink-based self-service data platforms, or equivalent (i.e. with Ray, or building spark- or Flink-like frameworks themselves in Scala, Akk, etc.)

• Strong familiarity with (and ample hands-on experience tuning & optimizing) at least one applicable technology in the Apache Hadoop ecosystem: Spark, Kafka, Hive/Iceberg/Delta Lake, Presto/Trino, Pinot, Druid, etc.

• Ample experience coding in Java, Scala, Kotlin or another JVM language (bonus points for experience tuning the language, i.e. garbage collection, memory management…)

• Production experience with relational SQL and NoSQL databases, including Postgres/MySQL, Cassandra/DynamoDB, etc.

• Intimate knowledge of deployment tools like Terraform and configuration automation tools like Chef/Puppet/Ansible

• Demonstrated track record of leadership and building a strong core engineering team

• Building and managing cloud and on-premise Hadoop and Spark infrastructure which could run on bare metal or Kubernetes clusters

• Strong cross-team collaboration and interpersonal skills working with various roles including engineering, product management, support and sales engineering

• Demonstrated ability to attract and hire talent and grow the team rapidly

• Experience working with remote teams and individuals while ensuring agility and code velocity

• Ability to communicate and articulate crisply at all levels from executive staff to engineers

• Broad general knowledge of the high-technology industry gained in larger enterprise software environments enhanced by ongoing awareness of R&D practices / technology advances

• Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes.

Bonus Points:

• Management experience with any of the following, the more the better

• Developing compute and storage layers for data platforms at high scale (batch or near Real-time)

• Experience with hybrid cloud environments (cloud and data center)

• Exposure to container orchestration technologies, like Kubernetes

• Experience with remote work, particularly managing distributed teams across multiple timezones

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