Senior Performance Engineer

Senior Performance Engineer

Sapiens International Corporation (NASDAQ and TASE: SPNS) is a leading global provider of software solutions for the insurance industry, with an emerging focus on the broader financial services sector. We offer core, end-to-end solutions to the global general insurance, property and casualty, life, pension and annuities, and retirement markets, as well as business decision management software. We have a track record of over 30 years in delivering superior software solutions to more than 400 financial services organizations. The Sapiens team of approximately 2,500 professionals operates through our fully owned subsidiaries

Description

The Senior Performance Engineer is responsible for validating the scalability, responsiveness, stability, and resilience of insurance applications through disciplined performance engineering, robust workload design, and deep technical analysis. This role exists to improve release confidence, reduce production risk, and ensure that systems continue to perform reliably under expected, peak, and stress conditions across user journeys, APIs, integrations, and data-intensive processes.

The role requires strong expertise in performance test strategy, scripting, execution, observability, bottleneck diagnosis, and root cause analysis across enterprise platforms. It is suited to a hands-on senior engineer who can translate system architecture and business-critical usage patterns into meaningful performance coverage, identify capacity and stability risks early, and help teams deliver systems with greater efficiency, predictability, and customer confidence.

The Senior Performance Engineer will leverage modern Agile methodologies, observability tooling, CI/CD-aligned practices, and AI-augmented analysis to accelerate performance feedback, strengthen engineering decisions, and deliver high-confidence software to clients.

Key Responsibilities

• Define and drive performance testing strategy for insurance applications, ensuring meaningful coverage across load, stress, endurance, scalability, and stability scenarios.
• Design, develop, execute, and maintain performance test scripts and scenarios using approved performance engineering tools and scripting languages.
• Translate architecture, usage patterns, integrations, batch behaviour, and business-critical transactions into realistic workload models and measurable performance objectives.
• Analyse application, infrastructure, database, and API behaviour under load to identify bottlenecks, degradation patterns, concurrency risks, and capacity constraints.
• Perform in-depth root cause analysis of performance issues by correlating test results with logs, traces, database behaviour, and APM insights.
• Generate clear performance reports, findings, and recommendations that help engineering, product, and delivery teams make informed release and tuning decisions.
• Collaborate closely with developers, architects, DevOps, product owners, and project teams to improve system efficiency, reliability, and non-functional readiness.
• Use SQL and database analysis to validate data volumes, transaction behaviour, and performance-impacting patterns across large datasets.
• Deploy, monitor, and interpret performance telemetry using APM and observability tools such as Dynatrace, Datadog, AppDynamics, Grafana, or equivalent platforms.
• Contribute to integrating performance testing into the software development lifecycle and release process so that risks are identified earlier and resolved faster.
• Recommend improvements to application design, configuration, infrastructure usage, and runtime tuning based on observed bottlenecks and evidence.
• Support continuous improvement of performance engineering practices, tools, reusable assets, baselines, and reporting standards.
• Mentor less experienced engineers through technical guidance, troubleshooting support, and stronger engineering discipline in performance analysis.
• Use approved AI/LLM tools to accelerate workload design, scenario expansion, SQL/query drafting, log interpretation, and initial performance hypothesis generation, while validating outputs for correctness and technical fit.
• Use AI-assisted diagnostics to cluster patterns, surface anomalies faster, and improve turnaround during performance triage and root-cause investigation.
• Contribute to performance validation in cloud-hosted and SaaS-style environments by assessing environment-specific behaviour, scalability characteristics, release impacts, and multi-tenant or shared-resource considerations where applicable.

Must Have Skills & Qualifications

Experience & Background

• 3+ years of hands-on experience in performance engineering or performance testing of enterprise applications.
• 3+ years of experience with performance testing tools such as JMeter, LoadRunner, NeoLoad, OctoPerf, or similar platforms.
• Experience working with web applications, APIs, multi-tier systems, and enterprise application architectures.
• Experience in insurance products or insurance technology platforms is preferred; policy, billing, claims, digital, or transaction-heavy platform exposure is valuable.
• Exposure to Agile or iterative software delivery environments and cross-functional engineering collaboration.

Technical & Functional Skills

• Strong automation testing fundamentals, including script development, framework maintenance, regression automation, integration testing, and end-to-end test execution.
• Strong performance engineering fundamentals across load, stress, endurance, and scalability testing.
• Ability to design robust and realistic performance test cases that effectively surface performance and reliability risks.
• Hands-on experience with performance scripting and test execution using one or more relevant tools.
• Strong analytical ability to interpret response times, throughput, resource utilization, concurrency behaviour, and system degradation trends.
• Strong SQL skills and experience writing complex queries to analyse data populations and performance-related behaviour.
• Working knowledge of relational and, ideally, NoSQL database concepts in performance analysis contexts.
• Experience with APM and observability tools such as Dynatrace, Datadog, AppDynamics, Grafana, or similar solutions.
• Working knowledge of Linux platforms for monitoring, troubleshooting, and environment-level analysis.
• Strong understanding of REST/SOAP services, APIs, web technologies, and application architecture.
• Familiarity with programming or scripting in Java, Python, JavaScript, Groovy, or similar languages used in performance engineering.
• Understanding of Docker, Kubernetes, networking, and runtime environments as they affect scalability and system behaviour.
• Familiarity with CI/CD concepts and how performance testing can be embedded into broader engineering workflows.
• Ability to convert performance findings into practical remediation recommendations for engineering and architecture teams.

AI-Related Expectations

• Use AI tools to refine performance scenarios, expand workload variations, and accelerate creation of supporting SQL queries or validation logic.
• Use AI support for log interpretation, defect clustering, and early bottleneck hypothesis generation during investigation cycles.
• Validate AI-generated outputs for accuracy, hallucination risk, technical feasibility, and alignment with actual architecture and workload behaviour before use.
• Follow approved privacy, security, and responsible-AI guidelines in all AI-assisted performance engineering activities.

SaaS & Cloud Awareness

• Understanding of cloud-hosted and SaaS delivery models, including environment-based deployments, release cadence, scalability expectations, and shared-resource considerations.
• Awareness of how containerized and distributed environments influence performance baselining, observability, and bottleneck diagnosis.
• Familiarity with how configuration-driven changes, infrastructure elasticity, and multi-environment deployments can affect non-functional behaviour.

Communication & Collaboration

• Strong verbal and written communication skills.
• Ability to present technical findings, bottlenecks, and remediation recommendations clearly to developers, architects, product teams, and other stakeholders.
• Ability to work independently while collaborating effectively across distributed and cross-functional teams.
• Strong reporting discipline and the ability to communicate performance risks with clarity, evidence, and business relevance.

Education

• Bachelors degree in engineering, Computer Science, Information Technology, or a related field.

Personal Attributes

• Strong ownership mindset and accountability for non-functional quality outcomes.
• Proactive, self-motivated, and organized in execution.
• Deeply analytical, evidence-driven, and structured in problem-solving.
• Curious about new tools, techniques, and ways to improve performance engineering maturity.
• Collaborative and credible in working with highly technical stakeholders.
• Able to balance rigor, speed, and practicality in a delivery environment.

Preferred / Good to Have

• Familiarity with containerized deployments, cloud environments, and performance testing in distributed architectures.
• Experience embedding performance checks into CI/CD pipelines.
• Exposure to Python for test scripting or analytics.
• Testing or relevant technical certifications are an advantage.
• Willingness to work with distributed teams and support implementation or release needs across time zones when required.

Why Join Us:

• Work on business-critical insurance platforms where performance directly affects customer experience, operational stability, and release confidence.
• Gain exposure to complex architectures, enterprise-scale transactions, and high-impact non-functional engineering challenges.
• Build depth across performance engineering, observability, diagnostics, and cloud-aware quality practices.
• Grow in an environment that is increasingly combining rigorous performance engineering with AI-assisted analysis and modern SaaS delivery expectations.