Job Description

We're a newly created engineering organisation on a mission to empower and accelerate autonomous value creation through insights and ML across the group. Our main users are the data scientists and analysts who sit inside the business units and support them in developing and re-developing their financial and digital products to better meet the needs of our clients and our shareholders. Our other users are the data management and data product teams who produce the data sets analysts and scientists need to launch straight into their work.The tribe is made up of 3 different engineering squads covering everything from how we ingest and store data, to how we develop and share ML artefacts and insights. We have one for ingestions, one for data storage and processing and one for insights & ML tooling. We have one data engineering talent pipeline for the whole tribe, where placements into specific squads are explored later in the interview process based on squad needs and candidate abilities. Data engineers report to the tech leads of the squad they are embedded into.Responsibilities

• Build configurable data ingestion platforms to collect and protect data from any source within two weeks or less. The team has bought and built to assemble a tool soup of persistent and secure ingestion tools, optimized for cost efficiency, with reliability as a non-negotiable standard. Success will be measured by the consistent achievement of the two-week ingestion target while maintaining high reliability and security.
• Engineer the platform for observability and performance. Make your systems observable from the outside, taking ownership of their performance. Facilitate real-time tracking of the system's internal state through logs, metrics, and traces. Define clear and measurable Service Level Indicators (SLIs) and Service Level Objectives (SLOs), and integrate these into team OKRs to drive excellence. Success will be measured by meeting or exceeding defined performance and reliability benchmarks, and the quick identification and resolution of issues.
• Engineer the platform with privacy and security by design. Secure the platform through compliance engineering initiatives that reduce the time and cognitive load related to compliance. Implement our Data Governance and Privacy policies effectively, leveraging encryption, firewalls, VPCs, Role Based Access Control and IAM integrations such as Active Directory. Success will be measured by the platform's adherence to security and privacy standards, with minimal compliance overhead for users.
• Leverage containerization and infrastructure as code. Implement containerization strategies and infrastructure as code (IaC) practices to ensure scalable, repeatable, and efficient deployment of data services. Utilize tools like Docker and Terraform to automate infrastructure management and streamline the development pipeline. Success will be measured by the consistency and reliability of deployments, reduced deployment times, and increased system scalability.
• Contribute to technology decisions. Engage in technology spikes to clarify needs and articulate solution criteria. Evaluate different options through hands-on experimentation, leading to documented evaluations and recommendations that are understood by technology leaders or users across the company. Success will be measured by the quality and clarity of your contributions to technology decisions and the adoption of your recommendations.
• Invest in your own development. Execute on a culture of excellence and continuous improvement for both your part of the platform and your own abilities. Live our culture of ownership, curiosity, and continuous learning. Participate in pre and post-mortems, sharing lessons learned across the organization along with how you are improving based on those lessons. Success is measured by your active participation in continuous learning initiatives and the tangible improvements in platform performance and personal growth.

• Technical leadership and coaching. Enable a culture of continuous learning and assist other engineers in their development journey.

RequirementsQualifications

• Matric
• National Diploma in an Information Technology related discipline or a Bachelors degree in Computer science, Statistics, Informatics, Information Systems, or a quantitative field will be recommended.

Experience

• 5+ years experience as a Data Engineer in data platform or service engineering team with at least 2 in a modern context with cloud technologies.
• Track record of high impact and investing in your own development.

Skills & Attributes

• Ability to act independently and to deal with high levels of ambiguity.
• You look for ways to do, instead of reasons not to.
• Where some see impossible, you see a way. You have the strong conviction in our ability to innovate.You turn obstacles into opportunity, chance into change.
• You move fast and fix things. You publish early and often, when you fail you learn and go again.
• You love creating more with less.
• You seek out and value different perspectives.

Search for:New JobsSubmit your CVIf you would like to submit your CV for consideration, please fill out this form and we will get in touch with you shortly.

5th Dimension Consulting