Jobs at Absa Group

The purpose of the role is to work embedded as a member of squad or; across multiple squads to produce, test, document and review algorithms & data specific source code that supports the deployment & optimization of data retrieval, processing, storage and distribution for a business area.

Key Responsibilities

Job Description
Accountability Data Architecture & Data Engineering

• Understand the technical landscape and bank wide architecture that is connected to or dependent on the
• business area supported in order to effectively design & deliver data solutions (architecture, pipeline etc.)
• Translate / interpret the data architecture direction and associated business requirements & leverage expertise in analytical & creative problem solving to synthesise data solution designs (build a solution from its components) beyond the analysis of the problem
• Participate in design thinking processes to successfully deliver data solution blueprints
• Leverage state of the art relational and No-SQL databases as well integration and streaming platforms do deliver sustainable business specific data solutions.
• Design data retrieval, storage & distribution solutions (and OR components thereof) including contributing to all phases of the development lifecycle e.g. design process
• Develop high quality data processing, retrieval, storage & distribution design in a test driven & domain driven / cross domain environment
• Build analytics tools that utilize the data pipeline by quickly producing well-organised, optimized, and documented source code & algorithms to deliver technical data solutions
• Create & Maintain Sophisticated CI / CD Pipelines (authoring & supporting CI/CD pipelines in Jenkins or similar tools and deploy to multi-site environments – supporting and managing your applications all the way to production)
• Automate tasks through appropriate tools and scripting technologies e.g. Ansible, Chef
• Debug existing source code and polish feature sets.
• Assemble large, complex data sets that meet business requirements & manage the data pipeline
• Build infrastructure to automate extremely high volumes of data delivery
• Create data tools for analytics and data science teams that assist them in building and optimizing data sets for the benefit of the business
• Ensure designs & solutions support the technical organisation principles of self-service, repeatability, testability, scalability & resilience
• Apply general design patterns and paradigms to deliver technical solutions
• Inform & support the infrastructure build required for optimal extraction, transformation, and loading of data from a wide variety of data sources
• Support the continuous optimisation, improvement & automation of data processing, retrieval, storage & distribution processes
• Ensure the quality assurance and testing of all data solutions aligned to the QA Engineering & broader architectural guidelines and standards of the organisation
• Implement & align to the Group Security standards and practices to ensure the undisputable separation, security & quality of the organisation’s data
• Meaningfully contribute to & ensure solutions align to the design & direction of the Group Architecture & in particular data standards, principles, preferences & practices. Short-term deployment must align to strategic long-term delivery.
• Meaningfully contribute to & ensure solutions align to the design and direction of the Group Infrastructure standards and practices e.g. OLA’s, IAAS, PAAS, SAAS, Containerization etc.
• Monitor the performance of data solutions designs & ensure ongoing optimization of data solutions
• Stay ahead of the curve on data processing, retrieval, storage & distribution technologies & processes

(Global best practices & trends) to ensure best practice

People

• Coach & mentor other engineers
• Conduct peer reviews, testing, problem solving within and across the broader team
• Build data science team capability in the use of data solutions

Risk & Governance

• Identify technical risks and mitigate these (pre, during & post deployment)
• Update / Design all application documentation aligned to the organization technical standards and risk governance frameworks
• Create business cases & solution specifications for various governance processes (e.g. CTO approvals)
• Participate in incident management & DR activity – applying critical thinking, problem solving & technical expertise to find the underlying cause of major incidents
• Deliver on time & on budget (always)

Education And Experience Required

• Relevant NQF level 7 qualification in computer science, engineering, physics, mathematics or equivalent
• Development and deployment of data applications
• Design & Implementation of infrastructure tooling and work on horizontal frameworks and libraries
• Creation of data ingestion pipelines between legacy data warehouses and the big data stack
• Automation of application back-end workflows
• Building and maintaining backend services created by multiple services framework
• Maintain and enhance applications backed by Big Data computation applications
• Be eager to learn new approaches and technologies
• Strong problem solving skills
• Strong programming skills
• Worked on Big Data platforms (Vanilla Hadoop, Cloudera or Hortonworks)
• Preferred: Experience with Scala or other functional languages (Haskell, Clojure, Kotlin, Clean)
• Preferred: Experience with some of the following: Apache Hadoop, Spark, Hive, Pig, Oozie, ZooKeeper, MongoDB, CouchbaseDB, Impala, Kudu, Linux, Bash, version control tools, continuous integration tools, SAS and SQL skills
• At least three (3) years’ experience working in Big data environment (advantageous for all, a must for high volume environments) – optimizing and building big data pipelines, architectures and data sets