Director of Engineering, AI Data Fabric

F5•San Jose, CA

1d•$253,400 - $380,200

About The Position

This role leads the engineering organization behind the F5 AI Data Fabric: the OpenTelemetry based data platform that ingests, processes, transforms, stores, and serves telemetry from both internal and external sources across F5’s businesses and platforms. AI/ML, analytics, and data engineering workloads across the company build on it, and it serves as F5’s system of record for product measurement and customer usage. You will lead its next evolution: extending the fabric into more first-party F5 data centers and additional cloud providers, making the AI Data Fabric even more available and performant. This expansion means processing exponentially more data, from more sources; you will guide the fabric and its team to meet the next scale moment. Your organization owns the full modern data platform stack: ingestion, processing, transformation, storage, and retrieval. A partner team owns F5’s data catalog, which builds on top of the fabric; you own the platform that catalog depends on and the contracts between you. You will stay technical enough to engage credibly in architecture discussions, unblock critical issues, and evaluate trade-offs, without serving as the primary hands-on contributor.

Requirements

Be kind.
Lead teams through ambiguity and change, turning ill-defined problems into clear priorities and plans. Execution and results is the standard by which you judge yourself.
Influence cross-functional and executive stakeholders as a clear, candid communicator, and develop the people managers who report to you.
Distributed compute and streaming at production scale: Kafka, Flink, Spark.
Analytical storage and query engines at scale (ClickHouse, StarRocks, Druid, Trino) and open table formats (Iceberg, Delta).
Kubernetes at production scale as a platform substrate, not a product surface: cluster and fleet management, scheduling, upgrades, and capacity.
Hybrid, multi-cloud infrastructure at production scale: two or more public clouds alongside on-premise or first-party data centers, with IaC, residency and sovereignty constraints, and workload portability.
OpenTelemetry pipelines: collectors, semantic conventions, and configuration.
Observability, alerting, and incident response for production data pipelines, including setting organizational standards.
Data security and privacy for a production data platform.
Working expertise in Golang, Java, Python, or C++, sufficient to engage credibly in technical reviews.
10+ years in software engineering, including 6+ years managing engineers and 2+ years managing managers.
Experience leading an engineering organization through a major platform migration, expansion, or re-architecture without disrupting production.
Track record of delivering and operating enterprise-scale data platforms under formal SLOs and 24/7 accountability.
Experience growing engineering managers: leveling, promoting, and managing performance at the manager layer.
Experience presenting technical strategy, risk, and investment trade-offs to executive leadership.
Experience engineering data correctness into production pipelines: validation, reconciliation, and automated detection of quality regressions.
Experience designing platform APIs and data contracts consumed by AI/ML, analytics, and data engineering teams, including training and feature data delivery.
Experience operating a platform whose data feeds billing or externally reported figures.

Responsibilities

Build, lead, and develop your engineering managers: set goals, coach for growth, evaluate performance, and hold each to a high technical and delivery bar.
Drive exceptional delivery across your teams: allocate capacity to the highest-value work, track execution and surface risk early, and clear the cross-team dependencies no single manager can resolve.
Partner with Product, Finance, SRE, Security, and engineering leadership to turn measurement needs into platform strategy and resourcing.
Champion pragmatic AI use: deploy LLMs and other techniques that speed feedback loops safely, and have your managers cascade the practice.
Measure everything, and hold yourself and your teams accountable to their commitments.
Own the technical strategy, multi-year roadmap, and engineering standards, including build-versus-buy and core technology selection.
Own vendor and licensing relationships: select partners, negotiate terms with Procurement and Finance, and hold vendors to their commitments.
Lead the fabric’s expansion across more first-party F5 data centers and additional cloud providers: phased regional rollout, residency requirements, and one coherent platform rather than per-environment forks.
Own the platform contracts that AI/ML, analytics, data engineering, and the catalog team depend on, and prioritize onboarding of new telemetry sources across teams.
Own the reliability, accuracy, timeliness, and auditability of telemetry across every source the fabric onboards.
Own production health: SLOs for ingestion, processing, and retrieval; on-call and incident command; and durable post-incident fixes.
Own the platform’s security and privacy posture: data classification, PII handling, access control, threat modeling, and audit readiness.
Own platform efficiency and cost: infrastructure spend, unit economics, and the capacity decisions that keep expansion affordable.