A
generalist recruitment company with specialised divisions acquiring the markets
leading talent in engineering, renewable energy, manufacturing, FMCG
manufacturing, finance, insurance, production, construction and mining.
Data Ops/ML Engineer
Minimum Requirements:
- Matric, with a
degree in Computer Science, Business Informatics, Mathematics, Applied
Statistics/Applied Mathematics Physics, or Data Engineering.
- Certification in
data, data science or artificial intelligence Experience/ Requirements
- 3 to 5 + years
of data engineering experience
- 3 to 5 + years
of Machine learning/ Artificial Intelligence and Statistical algorithm
development.
- 3+ years of
experience with any data warehouse technical architectures, ETL/ELT, and
reporting/analytics tools including, but not limited to, any of the
following combinations (1) SSIS and SSRS, (2) SAS ETL Framework, (3) SAP
ETL Framework, (4) MongoDB ETL deployments, (5) Apache Spark and Apache
Hive deployments will be beneficial.
- Working with
large volumes of structured and unstructured data and leveraging them to
build artificial intelligence (AI)/machine learning (ML) and predictive
modelling (PM) solutions through end-to-end automated data pipelines.
- Deep and
Extensive AWS knowledge and skills: Glue, S3, Lambda, IAM, Cloudformation
- The candidate
having DBA ability and knowledge across at least 2 platforms (example:
TSQL, SAS, PSQL, IBM VSAM and DB2 etc.) will also be beneficial.
- Some experience
with the Python programming language.
- Experience with
designing and implementing Cloud (AWS) solutions including use of APIs
available.
- Some experience
with Dev/OPS architecture, implementation and operation would be
advantageous.
- Minimum
bachelors degree in quantitative management (decision sciences) or
Computer Science/Statistics/Applied Statistics/Applied Mathematics
- Knowledge of
Engineering and Operational Excellence using standard methodologies.
- Best practices
in software engineering, data management, data storage, data computing and
distributed systems to solve business problems with data.
- Some experience
in applying SAFe/Scrum/Kanban methodologies would be advantageous.
- Knowledge and
understanding of business process management lifecycle which covers the
design, modeling, execution, monitoring, and optimization as well as
business process re-engineering.
- Good
problem-solving skills: The ability to exercise judgment in solving
technical, operational, and organizational challenges, to identify issues
proactively, to present solutions and options leading to resolution
- Good
programming, performance tuning and troubleshooting skills, using the
latest popular programming languages such as Python, scala, java and suite
of Microsoft languages C# and F# preferable
Responsibilities:
- Undertake the
processing of structured and unstructured data to utilize in data science
activities.
- Support the data
analysis of large information to discover trends and patterns and provide
feedback to the business.
- Assist in
designing and implementing scalable and robust processes for ingesting and
transforming datasets.
- Design,
implement, and support the creation and maintenance of data pipelines from
a multitude of sources.
- Ingest large,
complex data sets that meet functional and non-functional requirements.
- Implement and
train machine learning (ML), predictive analytics, data mining, and
artificial intelligence (AI) models to perform predictions and forecast
behavior and transactions, via the use of data, to enable the business to
be proactive in decision-making.
- Working with
large volumes of structured and unstructured data and leveraging them to
build artificial intelligence (AI)/machine learning (ML) and predictive
modelling (PM) solutions through end-to-end automated data pipelines.
- Enable the
business to solve the problem of working with large volumes of data in
diverse formats, and in doing so, enable innovative solutions.
- Design and build
bulk and delta data lift patterns for optimal extraction, transformation,
and loading of data.
- Supports the
organisations cloud strategy and aligns to the data architecture and
governance including the implementation of these data governance
practices.
- Engineer data in
the appropriate formats for downstream customers, risk, and product
analytics or enterprise applications.
- Development of
APIs for returning data to Enterprise Applications.
- Assist in
identifying, designing, and implementing robust process improvement
activities to drive efficiency and automation for greater
scalability.
- This includes
looking at new solutions and ways of working and being at the forefront of
emerging technologies.
- Work with
various stakeholders across the organization to understand data
requirements and apply technical knowledge of data management to solve key
business problems.
- Provide support
in the operational environment with all relevant support teams for data
services.
- Create and
maintain functional requirements and system specifications in support of
data architecture and detailed design specifications for current and
future designs.
- Support test and
deployment of new services and features.
Method of
Application
Interested and qualified? Go to Click Here to apply