Splunk Senior Staff Software Engineer - Performance Optimization & Innovation (PerfOpt)

Cisco; Kraków; On-site; PLN 360,000 - 420,000; Full time

Job summary

Splunk is seeking a senior data-performance engineer to join the PerfOpt team in Kraków. You will own performance across search, indexing, and data flows, shaping guidelines and designs for large-scale systems.

Lead end-to-end reliability improvements, design memory and concurrency strategies, and observability with profiling to quantify field impact and drive cross-team decisions. Contribute to scalable architectures and performance methodologies while mentoring engineers and collaborating with

Qualifications

• Expert-level C++ in production systems with memory management and allocators.
• Deep performance engineering across profiling, benchmarking, and analysis.
• Experience with latency optimization in distributed systems.
• Benchmarks and CI regression validation.
• Performance modelling and capacity planning.
• Architectural design to improve performance and scalability.
• Container performance with Docker and Kubernetes.
• Python tooling for benchmarking and telemetry.
• Linux performance internals (scheduler, memory, I/O).
• Git and CI automation (GitLab CI).

Responsibilities

• Serve as the performance authority for issues, roadmaps, reviews.
• Lead a performance domain end-to-end from hypothesis to production gains.
• Architect scalable subsystems in a distributed, petabyte-scale system.
• Own the performance methodology: profiling, analysis, benchmarking.
• Introduce observability with new telemetry and in-production profiling.
• Support leadership with data-driven recommendations.
• Drive automation and AI-enabled workflows to speed analysis.
• Mentor senior and staff engineers.

Skills

C++ expert

Performance engineering

Latency optimization

Benchmarking

Performance modelling

System design

Docker & Kubernetes

Python for tooling

Linux performance internals

Git & CI

Education

Bachelor's/Master's/PhD in related field

Tools

eBPF

VTune

perf

GitLab CI

Job description

Meet The Team

The Performance Optimization and Innovation (PerfOpt) team improves the Splunk customer experience through deep performance work, and sets Splunk's long-term performance standards through guidelines and design.

We work where performance is won or lost: search and indexing hot paths, cache and bloomfilter efficiency, S3 transfer throughput, I/O workload management, and parallelism in the data retrieval path. It's a C++ core moving petabyte-scale data across a large distributed architecture, and the results land in what customers feel - search latency, ingest throughput, and infrastructure cost. We are also the organization's performance center of gravity: the benchmarking, design patterns, and engineering standards other teams adopt come from here.

Your Impact

Serve as the performance authority for critical issues, roadmap planning, architecture reviews, and customer conversations. Set technical direction across teams.

Lead a performance domain end to end -hypothesis, instrumentation, design, measured customer-visible gain in production including cross-team problems nobody has framed yet.

Architect for scale - design and re-architect subsystems in a distributed, petabyte-scale system: memory hierarchy, concurrency, I/O, tail-latency.

Be responsible for the performance methodology - profiling and flame graph practice, lock contention analysis, benchmarking, CI regression gating.

Introduce observability - design new telemetry sources and bring eBPF and continuous profiling into production use, so performance is measurable in the field, not just the lab.

Enable leadership decisions - explain Directors and Managers on what the problem costs, what each option buys and costs, and the risk of inaction, with a recommendation you stand behind. A multi-week investigation becomes a one-page comparison they can act on in ten minutes.

Deliver fast and make everyone faster - work agentically, and build reusable AI agentic workflows and tooling that cut the team's analysis loops from days to hours. Navigate AI optimization: point agents at changes that move the numbers, catch work that fails under a profile, redirect quickly.

Lead and mentor senior and staff engineers.

Minimum Qualifications

Technical depth

• Expert-level C++ in production systems - memory management and allocators, move semantics, cache-friendly data structures, and a real command of the language's cost model.


• Deep performance engineering - depth across all of the below, production experience with several:

• CPU/memory profiling and flame-graph analysis (perf, eBPF, VTune), including off-CPU and continuous profiling.


• Lock contention resolution - hot mutexes, false sharing, atomics and memory ordering, lock-free techniques and when not to use them.


• Latency optimization in distributed architectures - tail latency, queuing, fan-out amplification, back-pressure, critical path analysis.


• Benchmarking - micro and workload benchmarks, sound test design and statistical analysis, sub-system validation CI regression detection.


• Performance modelling - analytical and capacity models that predict scaling limits and validate measured results.


• Architecture and system design - design and re-architect existing solutions for performance, scalability and operability, and defend those designs to a critical audience.


• Docker and Kubernetes - performance characterization in containerized environments.


• Strong Python for tooling, benchmark harnesses, telemetry pipelines, and performance data analysis.


• Linux performance internals - scheduler, memory subsystem, page cache, filesystems, block I/O, network stack.


• Git and CI (e.g. GitLab CI) for automating builds, tests, benchmarks, and releases.

Leadership

Track record of informing leadership decisions - quantifying trade-offs, surfacing risk early.

Distilling complex problems into comparable data points without losing the nuance or hiding uncertainty.

Clear, concise, high-signal communication across audiences - mechanism-level with engineers, trade-off-level with leadership, impact-level with customers.

AI savviness applied to delivery speed - daily use of AI coding agents on production work, building agentic workflows others adopt and setting the bar for verifying AI output against profiles, benchmarks, and telemetry.

Lead, mentor and grow engineers at every level - coaching junior engineers into ownership, and influencing senior and staff peers through technical leadership rather than authority.

Experience

Bachelor's + 12 years, Master's + 8 years, or PhD + 5 years of related experience, with specialized depth and breadth sufficient to advise management.

Preferred Qualifications

• Petabyte-scale data flow - ingest, storage, retrieval and search at scale, object storage (S3) access patterns, caching and eviction, I/O workload management.


• Splunk internals, or a comparable search, analytics, or large-scale data platform.


• Storage / query engine, or distributed search optimization.


• Hardware-level work - SIMD, NUMA, cache/TLB