The Apps Sup Tech Lead Analyst is a strategic professional who stays abreast of developments within own field and contributes to directional strategy by considering their application in own job and the business. Recognized technical authority for an area within the business. Requires basic commercial awareness. There are typically multiple people within the business that provide the same level of subject matter expertise. Developed communication and diplomacy skills are required in order to guide, influence and convince others, in particular colleagues in other areas and occasional external customers. Significant impact on the area through complex deliverables. Provides advice and counsel related to the technology or operations of the business. Work impacts an entire area, which eventually affects the overall performance and effectiveness of the sub-function/job family.

QUALIFICATIONS:

• 5-7 years of Data Analysis, or relevant experience, with at least 2 years as a Data Scientist, preferably with a focus on SRE or related fields.
• Strong understanding of SRE Fundamentals: Service Levels Concepts (SLI, SLO, SLA, Error Budget), Toil reduction, Automation, Observability primers, Chaos Engineering, and Production Management.
• Solid understanding of Observability Fundamentals: Golden Signals, Service Mapping, Business Transactions, Metrics-Logs correlations, Forecasting, and Trend analysis.
• Knowledge and experience of a variety of exploratory data analysis, predictive modelling, anomaly detection techniques, and their real-world advantages/drawbacks, particularly in the context of SRE.
• Experience using statistical computer languages like Python or Java, and familiarity with Go, Rust, Perl, Javascript, and Typescript.
• Working knowledge of financial products, automation solutions, and SRE principles. Experience with Linux, Docker, Kubernetes, Observability stacks (e.g., Grafana, ELK), Middleware (Kafka), and databases (Oracle).
• Machine learning experience for predictive alerting and automated incident mitigation is a plus.
• Experience in predictive model a/b testing and training models for SRE use cases.
• Experience in complex data extraction techniques and data cleansing methodologies, especially from monitoring and logging systems.
• Ability to deconstruct descriptive data into meaningful categories for predictive modeling and visual analysis related to system reliability.

EDUCATION:

• Bachelor’s/University degree, Master’s degree preferred
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MOST RELEVANT SKILLS

Please see the requirements listed above.
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OTHER RELEVANT SKILLS

For complementary skills, please see above and/or contact the recruiter.
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• The Apps Support Tech Lead Analyst is responsable to partner with multiple technology teams to ensure appropriate integration of functions to meet goals; identify and define necessary system enhancements; analyze existing system logic, identify problems; and recommend and implements solutions.
• Provides expertise in area and an advanced level of understanding of the principles of apps support.
• Formulates and defines systems scope and objectives for complex, high impact application enhancements and problem resolution through in-depth analysis and evaluation of complex business processes, systems and industry standards; documents requirements.
• Partners with multiple technology areas and management teams to ensure appropriate integration of functions to meet goals.
• Works closely with Product Owners, Business Analysts and Systems Analysts to determine and document Systems impacts and support requirements
• Considers the implications of the application of technology to the current environment.
• Identifies risks, vulnerabilities and security issues; communicates impact; proposes risk mitigation options.
• Mine and analyze large sets of data related to system performance, availability, and reliability, focusing on Golden Signals, Service Mapping, and Business Transactions.
• Identify gaps and opportunities for improvement in system reliability and performance using data-driven insights, applying trend analysis and forecasting techniques.
• Leverage data to develop strategic roadmaps for improving SRE practices and automation, with a focus on toil reduction.
• Define and deploy key SRE metrics (SLIs, SLOs, error budgets) for measuring process improvement and system reliability.
• Institute feedback models and automated alerting systems to enable continuous process improvement and proactive incident management.
• Conduct research, perform chaos engineering experiments, and publish artifacts to drive simplification and automation of SRE processes.