Data Engineer-Repost Full-time Job

Job Purpose

To create and maintain a consistent data architecture for the organisation and assist in making data assets visible, accessible and reusable through defining and promoting design principles and standards as well as leading design workshops in line with an organisation’s technology standards.

To ensure effective use of data analytics to optimise business practices, with particular emphasis on supporting the development and enhancement of data solutions across multiple channels.

Experience

Min: A minimum of 3 years’ experience in the database design and the extraction and utilisation of data.

Ideal: 5 years’ experience in data related roles with good exposure to different data stores

3 years plus in Data Integration using relevant technologies (eg, Pentaho Data Integration / Kettle) advantageous.

Exposure to various phases of software development life cycles and quality principles and best practices.

Expert level experience in relational database principles and associated querying languages.

Knowledge/ experience working in various SDLC (Systems Development Life Cycle) methodologies (eg, Waterfall, Agile)

Qualifications (Minimum)

Matric / Grade 12 / Equivalent NQF Level

An appropriate Bachelor's degree in Information Technology or a similar equivalent

Qualifications (Ideal or Preferred)

Knowledge

Min: Application of Data Quality principles, processes, and tooling

Ability to work across structured and unstructured data, extracting data and seamlessly blending disparate data sources, leveraging diverse ETL techniques and tools

In depth knowledge of the current ETL/Data Warehousing development processes and architecture

Comprehensive understanding of different Data Modeling techniques and Methodologies such as Normalisation, Dimensional Modelling and Data Vaults.

Excellent skills in Data Profiling, Data Cleansing and Data Quality Management

Thorough understanding of ITIL principles

Broad understanding of data stores ranging from SQL / NoSQL technologies through to Data Warehousing solutions

Solid understanding of DevOps principles (CI/CD)

Broad Understanding of enterprise grade security measures (eg. Auth / Auth)

Understand the principles of a Data Governance strategy and the associated Data Cataloguing principles and technologies

Ideal: Knowledge in Banking Industry advantageous

Application of Data Quality principles, processes, and tooling

Ability to work across structured and unstructured data, extracting data and seamlessly blending disparate data sources, leveraging diverse ETL techniques and tools

In depth knowledge of the current ETL/Data Warehousing development processes and architecture

Comprehensive understanding of different Data Modeling techniques and Methodologies such as Normalisation, Dimensional Modelling and Data Vaults.

Excellent skills in Data Profiling, Data Cleansing and Data Quality Management

Thorough understanding of ITIL principles

Broad understanding of data stores ranging from SQL / NoSQL technologies through to Data Warehousing solutions

Solid understanding of DevOps principles (CI/CD)

Broad Understanding of enterprise grade security measures (eg. Auth / Auth)

Understand the principles of a Data Governance strategy and the associated Data Cataloguing principles and technologies

Competencies

Communicating Effectively

Thinking Strategically

Making Decisions

Building Relationships

Driving Execution

Persuading & Influencing

PLEASE NOTE: All successful candidates will be subject to a criminal check as well as qualification and employment verification.