1 Banking jobs in South Africa on Job Mail

Job purpose description

 Responsible for overseeing junior data engineering activities and aiding in

building the organisations data collection systems and processing pipelines.

Oversee infrastructure, tools and frameworks used to support the delivery

of end-to-end solutions to business problems through high performing data

infrastructure.

Responsible for expanding and optimising the organisations data and data

pipeline architecture, whilst optimising data flow and collection to

ultimately support data initiatives.

Qualifications

Minimum qualification 1 Type of qualification: Post Graduate Degree

Field of study: Information Technology

Minimum qualification 2 Type of qualification: Post Graduate Degree

Field of study: Information Studies

Other minimum qualifications,

certifications or professional

memberships

NA

2

Preferred qualification 1 Type of qualification: Masters Degree

Field of study: Information Technology

Preferred qualification 2 Type of qualification: Masters Degree

Field of study: Information Studies

Other preferred qualifications,

certifications or professional

memberships

NA

Experience

Experience required 1 Job Function: Technology

Job Family: Data Monetisation

Years: 8-10 years

Experience Description: Experience with big data tools: Hadoop, Spark,

Kafka, etc. Experience with relational SQL and NoSQL databases, including

Postgres and Cassandra. Experience with data pipeline and workflow

management tools: Azkaban, Luigi, Airflow, etc. Experience with AWS cloud

services: EC2, EMR, RDS, Redshift. Experience with stream-processing

systems: Storm, Spark-Streaming, etc. Experience with object[1]oriented/object function scripting languages: Python, Java, C++, Scala, etc.

Experience required 2 Job Function: Technology

Job Family: Data Monetisation

Years: 8-10 years

Experience Description:

Experience required 3 Job Function: Technology

Job Family: Data Monetisation

Years: 8-10 years

Experience Description: Strong analytic skills related to working with

unstructured datasets. Build processes supporting data transformation,

data structures, metadata, dependency and workload management. A

successful history of manipulating, processing and extracting value from

large disconnected datasets. Working knowledge of message queuing,

stream processing, and highly scalable ‘big data’ data stores.

Total number of years

experience

8 years

Additional Job Dimensions

Number of direct reports Number: 1-3

Number of indirect reports Number: 1-10

Financial accountability Type of Budget: Project Budget

Size of Budget (ZAR equivalent): 500 000 - 2,5 million

Type of accountability: Contributes to budget management

Internal relationships Nature of relationship: Provide a service to them

Description or examples: Provide Data engineering guidance, information

services and ensure an effective data engineering capability, works closely

3

with data analysts and data scientists to ensure and effective data team.

Collaborate with technology and project teams.

External relationships Role type of external contact: Vendors

Description or examples: Manage SLA’s and technical service delivery of

vendors in the development, implementation and customer service

requirements for all Data Engineering requirements.

Nature of relationship: Manage the relationship

Work environment Physical Requirements 1: No specific physical requirements

Physical Requirements 2: Open plan office

Working Conditions 1: Africa Region travel may be required

Working Conditions 2: Domestic/local travel may be required

Regulatory Requirements

Regulated Role No

Regulations that apply None

Outputs

Data Owns and extends the business’s data pipeline through the collection,

storage, processing, and transformation of large data-sets and oversee the

process for creating and maintaining optimal data pipeline architecture

and creating databases optimized for performance, implementing schema

changes, and maintaining data architecture standards across the required  databases.

Data Oversee the assembly of large, complex data sets that meet functional /

non-functional business requirements and align data architecture with

business requirements.

Product Build analytics tools that utilise the data pipeline to provide actionable

insights into customer acquisition, operational efficiency and other key

business performance metrics. Create data tools for analytics and data

scientist team members that assist them in building and optimising

into an innovative industry leader.

Product Monitor the existing metrics, analyse data, and lead partnership with other

Data and Analytics teams in an effort to identify and implement system

and process improvements. Utilise data to discover tasks that can be

automated and identify, design, and implement internal process

improvements: automating manual processes, optimizing data delivery, re[1]designing infrastructure for greater scalability, etc.

Product Developing ETL processes that convert data into formats through a team of

data analysts and dashboard charts. Oversee large-scale data Hadoop

platforms and to support the fast-growing data within the business.

Data Responsible overseeing the process for enabling and running data

migrations across different databases and different servers and defines

and implements data stores based on system requirements and consumer

requirements.

4

Risk, Regulatory, Prudential and

Compliance

Responsible for performing thorough testing and validation in order to

Ensure proper data governance and quality across EDO and the business as

a whole.

Data Oversee, design, and develop algorithms for real-time data processing

within the business and to create the frameworks that enable quick and

efficient data acquisition. Deploy sophisticated analytics programs,

machine learning and statistical methods.

Data Manage the analysis if complex data elements and systems, data flow,

dependencies, and relationships in order to contribute to conceptual

physical and logical data models.

People Liaise with and collaborate with data analysts, data warehousing

engineers, and data scientists in finding and applying best practices within

the Data and Analytics department as well as defining the business’s data

requirements, which will ensure that the collected data is of a high quality

and optimal for use across the department and the business at large.

People Acts as a subject matter expert from a data perspective and provides input

into all decisions relating to data engineering and the use thereof. Provide

guidance in terms of setting governance standards.

Strategy Responsibility for contributing to the continual improvement of the

business’s data platforms through thorough observations and well[1]researched knowledge. Keeps track of industry best practices and trends

and through acquired knowledge, takes advantage of process and system

improvement opportunities.

Strategy Overseeing activities of the junior data engineering teams, ensuring proper

execution of their duties and alignment with vision and

objectives. Provide oversights and expertise to the Data Engineering that is

responsible for the design, deployment, and maintenance of the business’s

data platforms. Required to draw performance reports and strategic

proposals form his gathered knowledge and analyses results for senior

EDO leadership.

Behavioural Competencies (8– 12)

Behavioural competency 1 Competency Label: Providing Insights

Competency Description: This dimension is about providing insight with

regards to aspects that are likely to have an impact on the organisation. It is

about making it clear to others what the implications of internal and

external organisational environmental factors and processes are on the

competitive position of the organisation. “Providing Insights” should be

done with a focus on improving the situation.

Behavioural competency 2 Competency Label: Examining Information

Competency Description: This competency serves to aid effective problem

solving and requires being effective at probing and analysing situations

efficiently and accurately. This competency is important because without

sufficient analysis, effective solutions become less probable. In addition,

poor analysis makes it more likely that individuals become confused and

anxious, bored, error prone or overwhelmed by detail, which also impacts

negatively on successful problem solving.

Behavioural competency 3 Competency Label: Articulating Information

5

Competency Description: This competency is about effectively expressing

ideas and concerns, giving presentations, explaining things to others as well

as showing confidence in the interaction with other people, both strangers

and acquaintances alike.

Behavioural competency 4 Competency Label: Adopting Practical Approaches

Competency Description: Adopting practical solutions with an emphasis on

learning by doing. This competency requires individuals to utilise common

sense when required. Ultimately, this competency is important in order to

ensure that organisations implement feasible solutions.

Behavioural competency 5 Competency Label: Checking Details

Competency Description: This competency is concerned with the careful

checking and confirmation of details in a task. Another behaviour

associated with the “Checking Details” competency is being accurate. Being

accurate requires individuals to have a strong quality orientation as well as

to be thorough and detailed in their approach when completing tasks in

order to avoid making mistakes.

Behavioural competency 6 Competency Label: Challenging Ideas

Competency Description: This competency is about an individual facilitating

or catalysing change in an organisation. "Challenging Ideas" emphasises

individual behaviours associated with questioning assumptions, challenging

established views and arguing personal perspectives.

Behavioural competency 7 Competency Label: Exploring Possibilities

Competency Description: Exploring possibilities is about individuals being

effective at displaying behaviours associated with different situations or

problems. Individuals are required to look at a problem and define it in an

abstract manner. “Unpacking” a problem in terms of its underlying

principles and basing the problem on sound theory typically allows for

deeper insight into the true nature of the problem. This makes the nature

of the problem more complete, more meaningful and therefore longer

term sustainable solutions more likely

Behavioural competency 8 Competency Label: Team Working

Competency Description: This competency is about working well in a

team. In order to develop this competency, individuals are encouraged to

acknowledge the views and contributions of others, and to involve others

in decision-making.

Behavioural competency 9 Competency Label: Interpreting Data

Competency Description: This competency is about interpreting data

accurately with an emphasis on the processing and interpretation of

numbers. This competency also includes the utilisation of technology. Copy

description from Behavioural Competency Library

Technical Competencies

Technical competency 1 Competency Label: IT Architecture

Competency Description: Architectural methodologies used in the design

and development of IT systems.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry

6

Technical competency 2 Competency Label: Data Integrity

Competency Description: The ability to ensure the accuracy and

consistency of data for the duration that the data is stored as well as

preventing unintentional alterations or loss of data.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry

Technical competency 3 Competency Label: IT Applications

Competency Description: Knowledge and understanding of IT applications

and architecture.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry

Technical competency 4 Competency Label: Data Analysis

Competency Description: Ability to analyse statistics and other data,

interpret and evaluate results, and create reports and presentations for use

by others.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry

Technical competency 5 Competency Label: Knowledge Classification

Competency Description: The ability to apply metadata to information to

make it easy for other people to find.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry

Technical competency 6 Competency Label: Database Administration

Competency Description: Refers to the knowledge and experience required

to manage the installation, configuration, upgrade, administration,

monitoring and maintenance of physical databases.

Proficiency Level: EXPERT - Provides leadership in this field both within the

organisation and in the larger industry