Senior Performance Engineer

Role Overview

As a Senior Performance Engineer, you will be a technical leader within the Group Analytics Platform (GAP) Agile Release Train (ART), reporting directly to the Senior Software Architect. The GAP ART focuses on building and operating applications on the Group Data Hub Platform, an AWS-based Lakehouse Architecture, supporting enterprise-scale analytics and data integration capabilities.

This ART builds and operates data-intensive integration applications, including ingestion pipelines (ETL/ELT), data modeling and persistence, data exposure via APIs, and batch-based data extraction and processing. These applications leverage a modern cloud-native ecosystem and support critical business analytics and downstream consumers.

In this role, you will establish and lead the practice of performance engineering within the ART, owning the strategy, processes, tooling, measurement frameworks, and execution of performance engineering across multiple Scrum teams and technologies. You will partner closely with engineers, architects, product owners, and leadership to ensure the platform consistently meets latency, throughput, scalability, reliability, and cost objectives.

You will also play a key role in evaluating and adopting AI-assisted performance engineering and observability tooling, conducting proofs of concept, presenting findings to leadership, and driving adoption across the ART where appropriate. Success in this role requires deep technical expertise, strong leadership and communication skills, and the ability to influence.

Must Haves

• Extensive experience in performance engineering for complex, distributed, and dataintensive systems.
• Expertlevel knowledge of:
• System performance fundamentals (latency, throughput, concurrency, backpressure, resource utilization)
• Performance testing and modeling (load, stress, spike, capacity planning, growth forecasting)
• Observability and diagnostics (metrics, logs, traces, golden signals, RCA)
• Proven experience performance testing APIs and batch applications.
• Strong hands-on experience with performance and observability tools such as JMeter and New Relic.
• Ability to work effectively across multiple teams and technologies in a scaled Agile environment.
• Exceptional communication, leadership, and stakeholder management skills.

What You'll Do

Performance Engineering Strategy & Practice Leadership

• Define and own the performance engineering strategy for the GAP ART, including standards, best practices, and success metrics.
• Build and mature a repeatable performance engineering practice spanning design-time analysis, pre-release testing, and production monitoring.
• Establish performance SLAs/SLOs, performance budgets, and measurable acceptance criteria aligned with business outcomes.
• Act as the primary performance subject matter expert for the ART, advising architects and teams during design and planning.

Cross-Team Execution & Enablement

• Work across multiple Scrum teams to design, execute, and interpret load, stress, spike, and endurance tests for APIs, batch applications, and data pipelines.
• Guide teams in diagnosing and resolving performance bottlenecks across application, data, and infrastructure layers.
• Embed performance considerations into Agile ceremonies, PI planning, and architectural reviews.
• Coach and mentor engineers on performance fundamentals, tooling, and diagnostic techniques.

System Performance Analysis & Optimization

• Apply expert-level understanding of system performance fundamentals, including:
• Latency vs. throughput vs. concurrency
• Back-pressure and flow control
• CPU, memory, disk I/O, and network behavior
• Horizontal and vertical scaling strategies
• Analyze and optimize performance of data-intensive workloads, including:
• Query execution and optimization (Redshift, Spark, PostgreSQL)
• Ingestion pipelines and batch processing jobs
• API-based data access patterns
• Perform capacity modeling and growth forecasting to proactively identify scalability risks.

Observability & Diagnostics

• Define and evolve observability standards using metrics, logs, and distributed tracing.
• Leverage golden signals to detect, diagnose, and communicate performance issues.
• Lead root cause analysis efforts for performance-related incidents and near misses.
• Partner with platform and SRE teams to improve production monitoring and alerting.

AI-Assisted Tooling & Innovation

• Evaluate emerging AI-assisted performance, testing, and observability tools.
• Lead proofs of concept to assess value, accuracy, and applicability to the ART's ecosystem.
• Present findings, recommendations, and tradeoffs to technical and leadership audiences.
• Drive adoption of approved tools and integrate them into performance workflows and CI/CD pipelines.

Communication & Leadership

• Communicate performance risks, findings, and recommendations clearly to engineers, architects, and leadership.
• Translate technical performance data into business impact and decision-ready insights.
• Provide regular updates on the maturity, roadmap, and effectiveness of the performance engineering practice.

What You'll Bring

Required Skills & Experience

• Extensive experience in performance engineering for complex, distributed, and data-intensive systems.
• Expert-level knowledge of:
• System performance fundamentals (latency, throughput, concurrency, back-pressure, resource utilization)
• Performance testing and modeling (load, stress, spike, capacity planning, growth forecasting)
• Observability and diagnostics (metrics, logs, traces, golden signals, RCA)
• Proven experience performance testing APIs and batch applications.
• Strong hands-on experience with performance and observability tools such as JMeter and New Relic.
• Ability to work effectively across multiple teams and technologies in a scaled Agile environment.
• Exceptional communication, leadership, and stakeholder management skills.

Preferred Skills

• Experience with AWS-based data platforms, including services commonly used in lakehouse architectures.
• Knowledge of query engine behavior and optimization, including Redshift, Spark, and PostgreSQL.
• Experience with event- or messaging-based integrations (Kafka or similar) is a plus.
• Familiarity with CI/CD pipelines and integrating performance testing into delivery workflows.
• Experience evaluating or using AI-assisted tooling for performance analysis, testing, or observability.