Job Purpose

To both execute on and oversee multiple complex data initiatives, ensuring that relevant, high-quality data is available in a timely manner for various analytical processes.
A key aspect of this role is the ability to understand and translate business requirements into high value insightful data solutions, perform impact analysis to assess scope and budgeting implications, and engage effectively with key stakeholders across the bank.
The Senior Data Analytics Engineer must be adept at navigating the bank’s systems and data landscape, ensuring alignment between data initiatives and business objectives.
The senior Data Analytics Engineer is responsible for optimizing the data engineering analysis process, mentoring junior and intermediate analysts, conducting performance reviews, and supporting professional development.

Accountabilities

• Analytics Engineering Alignment
  • Navigating the bank’s systems and data landscape, ensuring alignment between data initiatives and business objectives.
  • Optimise the data engineering analysis process, mentoring junior and intermediate analysts, conducting performance reviews, and supporting professional development.
  • Lead the creation and maintenance of the enterprise frameworks, standards, processes and procedures applicable to specific data practices at an enterprise level, ensuring that data practices are aligned to industry, matured to the correct level and entrenched consistently within squads.
  • Drive understanding of the purpose, behaviour and user interactions with the data to optimize self-service offerings and improve Nedbank users' experience in accessing data, provide support to users in understanding and accessing data.
  • Provide overall Data Management Guidance and alignment to Nedbank's Data Management frameworks and standards.
  • Drive the Nedbank Data Strategy, Data Architecture roadmap, business strategy, objectives and values.
  • Define, establish and maintain the data practices strategy and framework
  • Define, establish and maintain the data policies, standards and guidelines for the practice.
  • Establish and maintain the processes, procedures and methods of delivery of the data practice.
• Design Data and Conceptual Modelling
  • Understand and translate business requirements into data solutions, perform impact analysis to assess scope and budgeting implications, and engage effectively with key stakeholders across the bank.
    Collaborate with Business teams, Data Scientists, Engineers, Modelers, Architects, and Governance and act as a key liaison between teams to ensure data discovery supports business insights required.
    Ensure initiatives across squads and teams are aligned with the business objectives and the data strategy is achieved.
    Advise stakeholders and other staff on application of data engineering analysis practices through consultation.
    Has an advanced understanding of Data, Object Oriented, Data Vault, Relational and Dimensional modelling concepts and techniques.
• Data Architecture
  • Oversee the designing, building and maintenance of scalable data architecture, including data models, schemas and metadata, to support efficient data storage, retrieval, and analysis.
  • Design, implement, and evolve robust and effectively aligned architecture solutions that operate in the business ecosystem.
  • Ensure good data governance practices are met as regards data quality in the form of evidenced data validation checks and data reconciliation and ensure that all metadata and lineage requirement have been considered and implemented
• Databases Specifications
  • Approve complex database specifications, ensuring that all agreed standards and protocols are followed and data integrity is preserved.
  • Suggest improvements for standards and protocols for database specifications.
  • Perform complex data testing and validation activities such as error resolution and data quality validation end to end through the data lifecycle.
  • Develop and implement strategies for data discovery and profiling to optimise understanding of the data to better enable further analytical processing.

Accountabilities Continue

• Databases Installation
  • Ensure installation and testing of complex and interrelated databases and associated products to ensure they are suitable for use and meet customer requirements.
  • Oversee the building and maintenance of APIs in collaboration with Data and/or Software Engineers.
  • Has an advanced understanding of the utilisation and adoption of Cloud Technologies and platforms in database Installation.
• Data Pipelines
  • Ensure good data governance practices are met as regards data quality in the form of evidenced data validation checks and data reconciliation and ensure that all metadata and lineage requirement have been considered and implemented.
  • Build and maintain complex data pipelines to efficiently extract data from multiple sources, in multiple formats and structures, and load target systems, whilst transforming data to a common model or structure, to provide data in a consistent, useable format to Nedbank data stakeholders.
  • Automate, monitor and improve the performance of data pipelines.
  • Lead the implementation of data lineage in the metadata hub for all data pipelines built.
  • Ensure that data validation and reconciliation checks are implemented in the data pipelines to maintain a high level of data accuracy, consistency and security.
• Data Protection
  • Ensure that all work adheres to regulatory requirements, maintaining compliance with all relevant data policies, privacy standards, and ownership guidelines.
• Data Analytics and insights Enablement
  • Design and apply advanced statistical techniques, analytical models, and data transformations to uncover trends, patterns, and drivers that inform strategic, tactical, and operational decisions.
  • Ensuring insights are relevant, trusted, and actionable for Enterprise use.
  • Develop reusable, governed analytical assets that enable consistent measurement, insight reuse, and scalability across the organisation.
  • Continuously improve analytics engineering practices by adopting modern data engineering, analytics, and modelling techniques to enhance insight quality and business impact.
• Talent Development
  • Mentor, guide, and provide training to Data Analytics Engineers to enhance their analytical and technical skills, manage their performance, growth and development.
  • Perform review on work performed by team members as well as review and govern work performed by other data teams.
  • When required, lead a team of Junior data Analytics Engineers and manage day-to-day activities, allocation, growth and development.
  • Partner with squads to manage the performance of resources.
  • Ensure a documented data skills framework exist to guide the upskilling of resources.
  • Ensure skill assessment is in place for practice resources and resources on squads.
  • Drive delivery of training content, and communication of the availability thereof, for the practice.

Essential Requirements

• Postgraduate Qualification
• General Experience: 6-10 years Substantial general work experience together with comprehensive job related experience in own area of expertise to fully competent level.
• Managerial Experience: 1-3 years Experience of supervising and directing people and other resources to achieve specific end results within limited timeframes.

Preferred Qualification

• Relevant postgraduate qualification in Data Science, Computer Science, Information Systems, Statistics, Business Analytics, Data Engineering, or a related quantitative/technology field.

Preferred Certifications

• Microsoft Certified: Fabric Analytics Engineer Associate
• Microsoft Certified: Azure Data Engineer Associate
• Databricks Certified Data Engineer Associate or Professional
• DAMA CDMP – Certified Data Management Professional
• Analytics Engineering Certification
• Data Vault 2.0 certification would be advantageous

Technical Expertise

• Enabling Self-Service Insights Works at an advanced level to create intuitive, governed data models and metrics, document them and empower stakeholders to self-serve insights. Typically works independently and provides guidance.
• Build Data Products Works at an advanced level to create reliable datasets/metrics/APIs, document and permission them, deploy to users, monitor and improve. Typically works independently and provides guidance.

• Design Data Products Operates as a recognised expert to discover user needs, define value and outcomes, specify data contracts, success metrics and plan the roadmap. Typically known as a subject matter authority.
• Data Visualisation Works at an advanced level to select the best data visualising tools to create accessible and accurate visuals and to turn these into a clear narrative, highlighting what matters and guide decisions. Typically works independently and provides guidance.
• Data Governance Principles Operates as a recognised expert to manage data in accordance with the organisation's data governance protocols, including sourcing, handling, classifying, storing, monitoring, extracting, loading, transporting and securing relevant data, while understanding and applying data quality principles to enhance the accuracy, consistency, and reliability of organisational data. Typically known as a subject matter authority.
• Business Data Modelling Works at an advanced level to conduct activities to collect, analyse, diagram (model) and report information and data flow, including state changes, to help make strategic decisions, achieve major goals, and solve complex problems. Typically works independently and provides guidance.

Behavioural competencies

• Collaborates: Builds partnerships and works collaboratively with others to meet shared objectives. For example, encourages coworkers and external partners to work together as a team, and makes sure they get credit for doing so. Encourages people to share their honest views, responds in a non-defensive way when they do.
• Ensures Accountability Holds self and others accountable to meet commitments. For example, measures and tracks team's and own performance, and helps the team learn from success, failure, and feedback. Adheres to, and enforces, goals, policies, and procedures.
• Optimises Work Processes Knows the most effective and efficient processes to get things done, with a focus on continuous improvement. For example, encourages and rewards continuous improvement and quality outcomes. Equips others to handle day-to-day tasks effectively on their own. Integrates systems to improve quality and service.
• Manages Complexity: Makes sense of complex, high quantity, and sometimes contradictory information to effectively solve problems. For example, asks questions to encourage others to think differently and enrich their analyses of complex situations. Accurately defines the key elements of complex, ambiguous situations.
• Directs Work: Provides direction, delegating, and removing obstacles to get work done. For example, delegates tasks, providing generally clear expectations to staff. Coordinates and integrates the team's work, reducing duplication. Measures team progress using the right indicators; recognizes when problems or shortfalls occur.