Latest Recruitment at Equity Bank Kenya

The Role Purpose

We are seeking a highly skilled and experienced ELK SRE Engineer to join our dynamic team. In this role, you will be responsible for the design, implementation, maintenance, and optimization of our Elasticsearch, Logstash, and Kibana (ELK) stack, ensuring its reliability, scalability, and performance. You will play a crucial part in providing robust logging, monitoring, and analytics solutions that are critical to our operational insights and incident response. 

Responsibilities: 

• ELK Stack Management: 

• Design, deploy, configure, and manage large-scale ELK clusters (Elasticsearch, Logstash, Kibana, Beats). 
• Ensure the high availability, scalability, and disaster recovery of ELK environments. 
• Monitor ELK cluster health, performance, and resource utilization, proactively identifying and resolving issues. 
• Perform regular upgrades and patching of ELK components. 
• Manage Elasticsearch indices, shards, mappings, and lifecycle policies. 
• Optimize Elasticsearch query performance and indexing strategies. 
• Troubleshoot complex issues related to data ingestion, search performance, and Kibana visualizations. 

Site Reliability Engineering (SRE) Principles: 

• Apply SRE principles to the ELK stack, focusing on automation, observability, and continuous improvement. 
• Develop and implement monitoring and alerting solutions for the ELK infrastructure and data pipelines. 
• Define and track Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for ELK services. 
• Conduct post-incident reviews to identify root causes and implement preventative measures. 

Data Ingestion and Pipelines: 

• Design, implement, and optimize data ingestion pipelines using Logstash, Beats (Filebeat, Metricbeat, Heartbeat, etc.), Kafka, or other relevant technologies. 
• Develop custom Logstash filters and configurations to parse, enrich, and transform log data. 
• Ensure data quality, integrity, and security throughout the ingestion process. 

Collaboration & Mentorship: 

• Work closely with development, operations, and security teams to understand their logging and monitoring requirements. 
• Provide expertise and guidance on best practices for using the ELK stack. 
• Create documentation, runbooks, and training materials for ELK users and administrators. 
• Mentor junior engineers and contribute to a culture of knowledge sharing. 

Automation: 

• Automate ELK deployment, configuration, and operational tasks using tools like Ansible, Terraform, Puppet, or Chef. 
• Develop scripts (Python, Go, Bash) to streamline common ELK administration tasks. 

Qualifications

Required Qualifications: 

• Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience. 
• 5+ years of experience working with and managing large-scale ELK (Elasticsearch, Logstash, Kibana) deployments. 
• Strong understanding of Elasticsearch architecture, performance tuning, and scaling strategies. 
• Proficiency in configuring Logstash pipelines and Beats for various data sources. 
• Experience with Kibana for dashboard creation, visualization, and alerting. 
• Solid experience with Linux/Unix operating systems. 
• Experience with Azure cloud platforms  
• Familiarity with SRE principles and practices, including SLOs, SLIs, and error budgets. 
• Strong problem-solving skills and the ability to troubleshoot complex distributed systems. 
• Excellent communication and collaboration skills. 

Preferred Qualifications: 

• Experience with Kafka or other message queuing systems. 
• Knowledge of other monitoring tools (Dynatrace, Datadog). 
• Familiarity with security best practices for the ELK stack. 
• Certifications related to Elasticsearch or cloud platforms.

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Description
Role Responsibility 

• Support in the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals. 
• Perform data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features.
• Document business requirements
• Develop model documentation for the purpose of model validation
• Develop dashboards and presentations for business insights using tools likePowerBI and Microsoft power point
• Utilise advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders.
• Designs various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives
• Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches. Communicate data information to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations.
• Create, maintain and optimise modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking. Drives sustainable and effective modelling solutions.
• Provideinput into Data management and modelling infrastructure requirements and adheres to the organisations’s infrastructure development processes, including the management of User Acceptance Testing (UAT). Conducts regression testing across all relevant systems as required.
• Build machine learning models from and utilises distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka

 Qualifications

 Qualifications and Experience

• Degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field. 
• Working experience in the finance industry via direct employment or consultancy 
• 3-5 years’ experience in working with structured and unstructured data (e.g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits. Experience with data visualisation tools, such as Power BI, Tableau, etc.
• Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Power BI; Qlikview; Tableau; SSIS SSRS, R, Python, JSON , C#, Java, C++, HTML
•  Experience with the use of GIT
• Proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles.
• Project management experience. Exposure to governance and regulatory matters as it relates to data. Experience in building models (credit scoring, propensity models, churn, etc.).
• A suitable candidate will also have had experience working with and influencing and possess vast experience and expertise with probability and statistics, inclusive of machine learning, experimental design, and optimization. As a bonus he will also have had experience working with Hadoop.
• Communication Skills: Communication skills will also be a necessity for the Data Scientist. He must be able to convey important messages and information
• Ms Office/Software:Outstanding skills in the use of Ms Word, Ms Excel, PowerPoint, and Outlook, which will all be necessary for the creation of both visually and verbally engaging reports and presentations, for senior data science management, executives, and stakeholders.
• The candidate must also demonstrate exceptionally good skills in SQL server reporting services, analysis services, PowerBI, integration services, Salesforce, or any other data visualization tools.
• Technological Savvy/Analytical Skills:Technologically adept and especially demonstrate an understanding of database and computer software.
• Interpersonal Skills: A suitable candidate for this position will be a team-collaborator, be result-oriented, be proactive and self-driven requiring minimal supervision, be open and welcoming to change, be a creative and strategic thinker, have innovative problem-solving skills, be highly organized, have an ability to handle multiple simultaneous tasks prioritize and meet tight deadlines, and demonstrate calmness in times of uncertainty and stress.
• People Skills:A people person who is able to form strong, lasting, and meaningful bonds with others people. This will make him/her an approachable and trustworthy individual who junior personnel readily follow and whoData and Analytics colleagues and stakeholders trust and who’s insights they give credit to, making execution of his duties that much easier.