Senior Data Modeller

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. Take ownership of complex analytics engineering tasks within a team; evolve schemas and pipelines in assigned areas, contribute to standards and patterns, ensure reliability and privacy-by-design and support peers with practical guidance.Job Responsibilities 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. Job Responsibilities 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.

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: Substantial general work experience together with comprehensive job related experience in own area of expertise to fully competent level. (Over 6 years to 10 years)

• Managerial Experience: Experience of supervising and directing people and other resources to achieve specific end results within limited timeframes (13 months to 3 years) 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