Responsible for building the organisations data collection systems and
processing pipelines.
Oversee data flow, infrastructure, tools and frameworks used to support the delivery of
end-to-end solutions to business problems.
Identify shortcomings and suggest improvements to existing processes, systems
and procedures.
Deliver a plan for change management program together with a project/program manager.
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.
Monitor the existing metrics, analyse data, and lead partnerships 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-designing infrastructure for greater scalability, etc.
Developing ETL processes that convert data into formats for consumption.
Responsible for executing testing and validation in line with data governance
and quality business requirements.
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.
Provide guidance in terms of setting governance standards.
Keeps track of industry best practices and trends and takes advantage of process and system improvement opportunities.
Makes sense of complex, high quantity, and sometimes contradictory information
to effectively solve problems.
Analyse statistics and other data, interpret and evaluate results, and create reports and presentations for use by others.
Provide technical guidance when required on costing, budgeting and finance tasks.
EXPERIENCE General Experience
IT Architecture:
Data Integrity:
Data Analysis:
Knowledge Classification:
The ability to apply metadata to information to make it easy for other people to
find.
Apache Spark. (supports programming languages Python, Scala, Java, and R)
C++.
Amazon Web Services.
Amazon S3.
Databricks
Database systems (SQL and NoSQL). Data engineer must know how to
manipulate database management systems (DBMS), which is a software
application that provides an interface to databases for information storage and
retrieval.
Data warehousing solutions.
ETL tools. ETL (Extract, Transfer, Load)
Data APIs. Allows two applications or machines to communicate with each other
for a specified task.
Python, Java, R and Scala programming languages.
Understanding the basics of distributed systems.
Desired Skills:
- IT Architecture
- Data integrity
- Data Analysis
- Metadata
- Apache Spark
- C++
- Amazon Web Services
- Amazon S3
- Databricks
- SQL
- NoSQL
- DBMS
- Data warehousing solutions
- ETL
- Data APIs
- Python
- Java
- R
Desired Qualification Level:
- Degree
About The Employer:
Leading telecommunications company listed on the JSE