Title:  Intermediate Analytics Engineer

Job Purpose

Design, build, and maintain data models and orchestrated pipelines for a defined domain; translate requirements into documented data flows and reusable models, add tests and observability, tune cost and performance and keep datasets timely and well-documented. Mentors and guides junior Analytics Engineers. 

Accountabilities

Analytics Engineering Alignment

• Drive understanding of the purpose, behaviour and user interactions with the data to optimise 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.
• Support the achievement of the Nedbank data strategy, data architecture roadmap, business strategy, objectives and values.

Design Data and Conceptual Modelling

• Displays a deep understanding of business data and information requirements, the related data flow, business processes and systems that generate the data required as well as the properties, profile and behaviour of the data.
• Partner with stakeholders to lead discovery and prioritisation, translate requirements into a data-product backlog with data contracts and acceptance criteria, manage trade-offs and ensure delivery aligns to measurable outcomes and SLAs.
• Collaborate with various teams including Data Modelers, Data Engineers, Data Architects and Data Governance to align data discovery insights and implement complex data solutions.
• Has an intermediate understanding of Data, Object Oriented, Data Vault, Relational and Dimensional modelling concepts and techniques.

Data Architecture

• Design, build, and maintain scalable data architecture, including data models, schemas and metadata, to support efficient data storage, retrieval, and analysis.
• Oversee data collection mechanisms and how they fit into data architecture, partnering with internal and external stakeholders to ensure quality and accuracy.
• Continuously refine data analysis techniques and explore new techniques, be familiar with all the tools and systems within the Nedbank Data Ecosystem, and enhance the quality of work.

Databases Specifications

• Approve database specifications, ensuring all agreed standards and protocols are followed and data integrity is preserved.
• Perform data testing and validation activities such as error resolution and data quality validation end to end through the data lifecycle.
• Provide detailed data specifications for downstream engineering tasks.
• Use data profiling techniques, contribute to the definition of data standards, identify potential data quality issues, report on and propose possible solutions.

Databases Installation

• Install and test complex databases and associated products to ensure they are suitable for use and meet customer requirements.
• Build and maintain APIs in collaboration with Data and/or Software Engineers.
• Has an Intermediate understanding of the utilisation and adoption of Cloud Technologies and platforms in database Installation.

Data Pipelines

• Build and maintain simple 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.
• Ensure that data lineage is implemented 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.

Essential Requirements

• Undergraduate Degree
• General Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years)
• Managerial Experience: Experience of general supervision of more junior colleagues (7 to 12 months)

Technical Expertise

• Enabling Self-Service Insights: Works with full competence to create intuitive, governed data models and metrics, document them and empower stakeholders to self-serve insights. Typically works without supervision and may provide technical guidance.
• Build Data Products: Works with full competence to create reliable datasets/metrics/APIs, document and permission them, deploy to users, monitor and improve. Typically works without supervision and may provide technical guidance.

• Design Data Products: Works at an advanced level to discover user needs, define value and outcomes, specify data contracts, success metrics and plan the roadmap. Typically works independently and provides guidance.
• 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: Works with full competence 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 works without supervision and may provide technical guidance.
• Business Data Modelling: Works with full competence 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 without supervision and may provide technical guidance.

Behavioural competencies

• Collaborates: Builds partnerships and works collaboratively with others to meet shared objectives. For example, enlists a range of stakeholders to add value; ensures they are well informed and surprises are avoided. Confronts and challenges "us vs. them"; shows strong appreciation for others' efforts toward shared goals.
• Ensures Accountability: Holds self and others accountable to meet commitments. For example, tracks performance and strives to remain effective, learning from both successes and failures. Readily takes on challenges or difficult tasks and has reputation for delivering on commitments.
• Optimises Work Processes: Knows the most effective and efficient processes to get things done, with a focus on continuous improvement. For example, pays close attention to a variety of metrics and benchmarks; determines both major and subtle ways to optimize processes. Swiftly resolves process breakdowns; takes steps to ensure that problems do not recur.
• Decision Quality: Makes good and timely decisions that keep the organization moving forward. For example, consistently demonstrates strong judgment; may be sought out by others for expertise and guidance. Takes smart, independent action in urgent and non-routine situations, knows when to escalate for others' involvement.
• Manages Complexity:  Makes sense of complex, high quantity, and sometimes contradictory information to effectively solve problems. For example, consistently looks at complex issues from many angles; obtains a rich and deep understanding; swiftly cuts to the core issue; skillfully separates root causes from symptoms.