Job Openings at Equity Bank Kenya

Responsibilities of the Senior Data Engineer 

• Oversee activities of the junior data engineering teams, ensuring  proper execution of their duties and alignment with the Company objectives.  
• Provide oversight and expertise to the Data Engineering that is responsible for the design, deployment, and maintenance of the business’s data platforms. Required to draw performance reports and strategic proposals form his gathered knowledge and analyses results for senior EDO leadership. 
• Own and extend the business’s data pipeline through the collection, storage, processing, and transformation of large data- sets and oversee the process for creating and maintaining optimal data pipeline architecture and creating databases optimized for performance, implementing schema changes, and maintaining data  architecture standards across the required databases. 
• Oversee the assembly of large, complex data sets that meet functional / non-functional business requirements and align data architecture with business requirements.  
• Oversee, design, and develop algorithms for real-time data processing within the business and to create the frameworks that  enable quick and efficient data acquisition. Deploy sophisticated analytics programs, machine learning and statistical methods. 
• Build analytics tools that utilise the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics. 
• Create data tools for analytics and data scientist team members that assist them in building and optimising into an innovative industry leader. 
• Monitor the existing metrics, analyse data, and lead partnership with other Data and Analytics teams in an effort to identify and implement system and process improvements.n  
• Utilise data to discover tasks that can be automated and identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. 
• Acts as a subject matter expert from a data perspective and provides input into all decisions relating to data engineering and the use thereof. Provide guidance in terms of setting governance standards. 
• Responsible for performing thorough testing and validation in order to Ensure proper data governance and quality across EDO and the business as a whole.

Qualifications

• Master’s degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field.  
• 8 to 10 years Experience in Technology handling data monetization; Including; big data tools: Hadoop, Spark, Kafka, relational SQL and NoSQL databases, including Postgres and Cassandra. 
• Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc. Experience with AWS cloud services: EC2, EMR, RDS, Redshift. 
• Experience with stream-processing systems: Storm, Spark- Streaming, etc. Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc. 
• The candidate must also have a proven and successful experience track record of leading high-performing data analyst teams leading through the successful performance of advanced quantitative analyses and statistical modeling that positively impact business performance. 
• Strong analytic skills related to working with unstructured datasets. Build processes supporting data transformation, data structures, metadata, dependency and workload management. A successful history of manipulating, processing and extracting value from large disconnected datasets. 
• Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores. 
• 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, Tableau, 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. 
• Must also be highly skilled in statistical and modeling packages such as SAS, Statistica, Matlab, R, visualization and other advanced analysis tools. He will also be an expert in data management programming such as SQL, PL-SQL, and Python as well as being familiar win the workings of motion-tracking data and time-series analyses. 
• Interpersonal Skills: A suitable candidate for this position will be a team-builder, 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 an approachable and trustworthy individual who junior personnel readily follow and who senior Data and Analytics executives and stakeholders trust and who’s insights they give credit to, making execution of his duties much easier. 

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Job Responsibilities/ Accountabilities:

• Implementing measures and a privacy governance framework to manage data handling and use in compliance with the all relevant privacy laws that Equity bank has to be compliant with across the region
• Working with key internal stakeholders in the review of projects to ensure compliance with local data privacy laws, and where necessary, complete and advise on privacy impact assessments
• Serving as the primary point of contact and liaison for the Data Commission Office and other Data Protection Authorities within the jurisdisctions that Equity bank operates in.
• Serving as the primary point of contact for queries in the business in regards to data Protection and Privacy.
• Reviewing Equity vendor contracts and consents needed to implement projects in partnership with the Bank’s Legal and Information Security functions, and ensuring filing requirements with local regulators are achieved
• Ensure fulfilment data Subject rights arising from the various touch points the bank has with the customer.
• Developing policies, standards and procedures that align to the requirements set out in the GDPR, Data Protection Act and any localization requirements in countries of operation
• Collaborating with the Information Security function to raise employee awareness of data privacy and security issues, and providing training on the subject matter across the group.
• Monitoring performance and providing advice on the impact of data protection efforts across the bank.
• Maintaining comprehensive records of all data processing activities conducted by the bank and group, including the purpose of all processing activities, which must be made public on request.
• Interfacing with Equity Bank customers  to inform them about how their data is being used, their Rights and what measures the company has put in place to protect their personal information.
• Collaborating with the Bank’s Information Security and Legal functions to maintain records of all data assets, ensure data classification and maintaining a data security incident management plan to ensure timely remediation of incidents, security breachs, complaints and claims.

Qualifications

KEY CRITICAL COMPETENCIES:

• Lead from the front
• Strong analytical skills.
• Adaptability.
• Excellent and effective communications skills, both orally and in writing.
• Reliability.
• Be efficient and effective in problem solving.
• Initiative.
• Planning and organization.

REQUIREMENTS

OTHER SKILLS and ABILITIES:

• Able to work on weekends, or extended hours
• The position requires participation in an on-call rotation and off-hours/weekend.
• Have strong communication skills to address customer phone calls and email inquiries
• High personal standards and goal oriented.

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• The Data Solutions Manager is responsible for leading the design, implementation, and optimization of scalable enterprise data architectures that support business intelligence, analytics, and digital transformation initiatives. This role ensures seamless integration of legacy systems with modern cloud-native platforms, promotes data governance and security, and drives the delivery of data solutions aligned with organizational goals. The incumbent will collaborate with cross-functional teams and stakeholders to build robust, future-ready data ecosystems within the banking and financial services environment.

Key Responsibilities 

• Solution Design & Architecture 
• Design end-to-end enterprise data solutions across customer, transactional, operational, and risk domains. 
• Develop reusable architecture blueprints and data integration patterns across multiple layers: ingestion, processing, semantic, and serving. 
• Evaluate and integrate open-source and vendor-based solutions into the data stack. 
• Platform Delivery & Implementation 
• Own delivery of data pipelines, data marts, APIs, and reporting interfaces that support business decision-making and automation. 
• Oversee DevOps/CI-CD practices for data deployments. 
• Lead PoCs and RFPs for emerging data technologies. 
• Standards, Governance & Security 
• Define architecture principles and data modeling standards (3NF, star schema, data vault). 
• Support Data Governance and Data Protection teams to ensure adherence to internal and regulatory data policies (e.g., GDPR, ODPC). 
• Ensure security protocols and data access controls are built into all solutions (RBAC, encryption, data masking).
• Business Engagement & Stakeholder Management 
• Act as a technical advisor to business units, interpreting their needs and aligning them to technical solutions. 
• Present architecture decisions and trade-offs to non-technical audiences including senior executives. 
• Collaborate with Data Privacy, Risk, and Compliance to ensure solutions support operational and regulatory requirements. 
• Performance Monitoring & Optimization 
• Continuously monitor and optimize data pipelines, ETL jobs, and query performance. 
• Leverage observability tools to proactively identify system bottlenecks and incidents. 
• Recommend architectural changes to support growth, cost efficiency, and business agility. 
• Team Development & Leadership 
• Coach and mentor junior architects, engineers, and analysts. 
• Foster a learning culture within the data engineering team through knowledge sharing and technical training. 
• Help shape the long-term talent development plan for the data solutions team.

Qualifications & Experience 

• Bachelor’s degree in computer science, Information Systems, Data Science or related field. 
• Minimum 3–5 years of hands-on experience in enterprise data architecture, solution design, and delivery within banking or financial services. 
• Proven experience implementing scalable data platforms both on-premise and on cloud (AWS, Azure, GCP). 
• Strong familiarity with modern data stacks: ETL/ELT frameworks, data lakes (e.g., Hadoop/S3), data warehouses (e.g., Snowflake, Azure Synapse). 
• Experience leading cross-functional delivery teams and working with Agile/Scrum methodologies. 
• Experience integrating legacy core banking systems with modern cloud-native data solutions. 

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Reporting to Head Data Science, the Senior Data Scientist will apply data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions. Execute intelligent automation and predictive modelling.

Responsibilities of the Senior Data Scientist:

• Direct 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.
• 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.
• Provide input into Data management and modelling infrastructure requirements and adheres to the organisation’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 Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka
• Act as a subject matter expert from a data science perspective and provides input into all decisions relating to data science and the use thereof. Educate the organisation on data science perspectives on new approaches, such as testing hypotheses and statistical validation of results. Ensure ongoing knowledge of industry standards as well as best practice and identify gaps between these definitions/data elements and organisation data elements/definitions

Qualifications

Qualifications and Experience:

• Degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field.
• 5-7 years’ experience in working with 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, such as SAS, R, SPSS, etc. 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
• 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.).
• The candidate must also have a proven and successful experience track record of leading high-performing data analyst teams leading through the successful performance of advanced quantitative analyses and statistical modelling that positively impact business performance.
• 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 Senior Data Scientist. He must be able to convey important messages and information down the line in order to ensure proper exception of duties by junior data science personnel.
• 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, Tableau, 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-builder, 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 other people. This will make him/her an approachable and trustworthy individual who junior personnel readily follow and who Data and Analytics colleagues and stakeholders trust and who’s insights they give credit to, making execution of his duties that much easier