[Hiring] Engineering Manager, Data Platform @LaunchDarkly

Role Description

We are seeking an Engineering Manager to lead LaunchDarkly’s Data Platform team and help shape the high-throughput data backbone behind our most important products. As Engineering Manager, you’ll lead a technically deep team building and operating the systems that ingest, process, and serve the real-time and batch data powering Experimentation, Metrics, Observability, Release Guardian, AgentControl, and Data Export.

Working primarily in Go and Python, your team tackles ambitious distributed-systems challenges with technologies including Kinesis, Airflow, Athena, and Iceberg on S3, ClickHouse, Elasticsearch, Terraform, AWS, and Datadog. From evolving real-time analytics and context data to strengthening streaming and batch pipelines, you’ll guide the platform that turns vast volumes of events into reliable, customer-facing product experiences while partnering across Core Engineering and product teams to make data faster, more resilient, and more useful for every LaunchDarkly customer.

Responsibilities

• Lead and develop a team of backend engineers, providing coaching, feedback, and career growth opportunities
• Own the delivery, correctness, and resilience of Data Platform's production systems, including Tier 0 ingestion endpoints and the pipelines and data stores behind them
• Partner with Product Management and consuming engineering teams to scope, estimate, and sequence roadmap work, making tradeoffs in real time
• Act as the primary communicator and point of contact for Data Platform with engineering leadership, partner teams, and customers
• Drive operational excellence: reliability, observability, cost, incident response, and on-call health
• Build team working norms that promote collaboration, reduce silos and bus factor, and keep engineers engaged
• Participate in hiring to grow the team and raise the engineering bar

Qualifications

• 8+ years of experience in software engineering, with at least 2 years managing a team of backend or infrastructure engineers
• Experience owning high-throughput, reliability-critical production systems such as event ingestion, streaming or batch pipelines, or large analytical data stores
• Strong distributed-systems fundamentals and the judgment to guide technical tradeoffs with senior engineers
• Proven ability to partner with Product Management to translate business goals into engineering plans with reliable estimates
• Track record of coaching and developing engineers, including performance management, career growth planning, and technical mentorship
• Strong communication skills in a distributed, cross-time-zone environment
• Familiarity with observability practices (metrics, tracing, alerting, structured logging) and comfort leading production incident response
• Experience with Go or Python, and with technologies such as Kafka or Kinesis, ClickHouse, Airflow, Athena or Iceberg, Elasticsearch, and Terraform is a plus

Requirements

• Target pay ranges based on Geographic Zones for Level 2:
• Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $191,800 - $263,670
• Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $172,600 - $237,270
• Zone 3: All other US locations - $163,000 - $224,070
• All Zones Inclusive of a 10% Bonus Target
• Exact compensation may vary based on skills, experience, and location.
• Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary.

Company Description

Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong.

• Improving the velocity and stability of software releases, without the fear of end customer outages
• Delivering targeted experiences by easily personalizing features to customer cohorts
• Maximizing the business impact of every feature through the ability to experiment and optimize
• Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types
• Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability