Data Engineer

Recruiter:

Recru-it

Job Ref:

PE010826/LM

Date posted:

Friday, June 7, 2024

Location:

Johannesburg, South Africa

Salary:

market related Monthly

SUMMARY:
Job Purpose:
The purpose of the Data Engineer is to leverage their data expertise and data related technologies, in line with the company Data Architecture Roadmap, to advance technical thought leadership for the Enterprise, deliver fit for purpose data products, and support data initiatives. In addition, Data Engineers enhance the data infrastructure of the company to enable advanced analytics, machine learning and artificial intelligence by providing clean, usable data to stakeholders. They also create data pipelines, Ingestion, provisioning, streaming, self-service, API and solutions around big data that support the company strategy to become a data driven organisation.

Job Responsibilities:

Responsible for the maintenance, improvement, cleaning, and manipulation of data in the companies operational and analytics databases.
Data Infrastructure: Build and manage scalable, optimised, supported, tested, secure, and reliable data infrastructure e.g. using Infrastructure and Databases (DB2, PostgreSQL, MSSQL, HBase, NoSQL, etc.), Data Lakes Storage (Azure Data Lake Gen 2), Cloud-based solutions (SAS, Azure Databricks, Azure Data Factory, HDInsight), Data Platforms (SAS, Ab Initio, Denodo, Netezza, Azure Cloud). Ensure data security and privacy in collaboration with Information Security, CISO and Data Governance
Data Pipeline Build (Ingestion, Provisioning, Streaming and API): Build and maintain data pipelines to:
create data pipelines for data integration (Data Ingestion, Data Provisioning and Data Streaming) utilising both On Premise tool sets and Cloud Data Engineering tool sets
efficiently extract data (Data Acquisition) from Golden Sources, Trusted sources and Writebacks with data integration from multiple sources, formats and structures
load the companies Data Warehouse (Data Reservoir, Atomic Data Warehouse, Enterprise Data Mart)
provide data to the respective Lines of Business Marts, Regulatory Marts and Compliance Marts through self-service data virtualisation
provide data to applications or companies Data consumers
transform data to a common data model for reporting and data analysis, and to provide data in a consistent, useable format to companies data stakeholders
handle big data technologies (Hadoop), streaming (KAFKA) and data Replication (IBM Inphosphere Data Replication)
drive utilisation of data integration tools (Ab Initio) and Cloud data integration tools (Azure Data Factory and Azure Data Bricks)
Data Modelling and Schema Build: In collaboration with Data Modellers, create data models and database schemas on the Data Reservoir, Data Lake, Atomic Data Warehouse and Enterprise Data Marts.
The Companies Data Warehouse Automation: Automate, monitor and improve the performance of data pipelines.
Collaboration: Collaborate with Data Analysts, Software Engineers, Data Modelers, Data Scientistsm Scrum Masers and Data Warehouse teams as part of a squad to contribute to the data architecture detail designs and take ownership of Epics end-to-end and ensure that data solutions deliver business value.
Data Quality and Data Governance: Ensure that reasonable data quality checks are implemented in the data pipelines to maintain a high level of data accuracy, consistency and security.
Performance and Optimisation: Ensure the performance of the companies data warehouse, integration patterns, batch and real time jobs, streaming and API's.
API Development: Build API's that enable the Data Driven Organisation, ensuring that the data warehouse is optimised for API's by collaborating with Software Engineers.

Essential Qualifications - NQF Level:

Advanced Diplomas/National 1st Degrees

Preferred Qualification:

Field of Study: BCom, BSc, BEng

Preferred Certifications:

Cloud (Azure, AWS), DEVOPS or Data engineering certification. Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist.

Minimum Experience Level:

Total number of years of experience:3 - 6 years
Type of experience: Experienced at working independently within a squad and has the demonstrated knowledge and skills to deliver data outcomes without supervision.
Experience designing, building, and maintaining data warehouses and data lakes.
Experience with big data technologies such as Hadoop, Spark, and Hive.
Experience with programming languages such as Python, Java, and SQL.
Experience with relational databases and NoSQL databases.
Experience with cloud computing platforms such as AWS, Azure, and GCP. Experience with data visualization tools. Result-driven, analytical creative thinker, with demonstrated ability for innovative problem solving.

Technical / Professional Knowledge

Cloud Data Engineering (Azure, AWS, Google)
Data Warehousing
Databases (PostgreSQL, MS SQL, IBM DB2, HBase, MongoDB)
Programming (Python, Java, SQL)
Data Analysis and Data Modelling
Data Pipelines and ETL tools (Ab Initio, ADB, ADF, SAS ETL)
Agile Delivery
Problem solving skills

Behavioral Competencies

Decision Making
Influencing
Communication
Innovation
Technical/Professional Knowledge and Skills
Building Partnerships
Continuous Learning

POSITION INFO:
Job Purpose:
The purpose of the Data Engineer is to leverage their data expertise and data related technologies, in line with the company Data Architecture Roadmap, to advance technical thought leadership for the Enterprise, deliver fit for purpose data products, and support data initiatives. In addition, Data Engineers enhance the data infrastructure of the company to enable advanced analytics, machine learning and artificial intelligence by providing clean, usable data to stakeholders. They also create data pipelines, Ingestion, provisioning, streaming, self-service, API and solutions around big data that support the company strategy to become a data driven organisation.

Job Responsibilities:

Responsible for the maintenance, improvement, cleaning, and manipulation of data in the companies operational and analytics databases.
Data Infrastructure: Build and manage scalable, optimised, supported, tested, secure, and reliable data infrastructure e.g. using Infrastructure and Databases (DB2, PostgreSQL, MSSQL, HBase, NoSQL, etc.), Data Lakes Storage (Azure Data Lake Gen 2), Cloud-based solutions (SAS, Azure Databricks, Azure Data Factory, HDInsight), Data Platforms (SAS, Ab Initio, Denodo, Netezza, Azure Cloud). Ensure data security and privacy in collaboration with Information Security, CISO and Data Governance
Data Pipeline Build (Ingestion, Provisioning, Streaming and API): Build and maintain data pipelines to:
create data pipelines for data integration (Data Ingestion, Data Provisioning and Data Streaming) utilising both On Premise tool sets and Cloud Data Engineering tool sets
efficiently extract data (Data Acquisition) from Golden Sources, Trusted sources and Writebacks with data integration from multiple sources, formats and structures
load the companies Data Warehouse (Data Reservoir, Atomic Data Warehouse, Enterprise Data Mart)
provide data to the respective Lines of Business Marts, Regulatory Marts and Compliance Marts through self-service data virtualisation
provide data to applications or companies Data consumers
transform data to a common data model for reporting and data analysis, and to provide data in a consistent, useable format to companies data stakeholders
handle big data technologies (Hadoop), streaming (KAFKA) and data Replication (IBM Inphosphere Data Replication)
drive utilisation of data integration tools (Ab Initio) and Cloud data integration tools (Azure Data Factory and Azure Data Bricks)
Data Modelling and Schema Build: In collaboration with Data Modellers, create data models and database schemas on the Data Reservoir, Data Lake, Atomic Data Warehouse and Enterprise Data Marts.
The Companies Data Warehouse Automation: Automate, monitor and improve the performance of data pipelines.
Collaboration: Collaborate with Data Analysts, Software Engineers, Data Modelers, Data Scientistsm Scrum Masers and Data Warehouse teams as part of a squad to contribute to the data architecture detail designs and take ownership of Epics end-to-end and ensure that data solutions deliver business value.
Data Quality and Data Governance: Ensure that reasonable data quality checks are implemented in the data pipelines to maintain a high level of data accuracy, consistency and security.
Performance and Optimisation: Ensure the performance of the companies data warehouse, integration patterns, batch and real time jobs, streaming and API's.
API Development: Build API's that enable the Data Driven Organisation, ensuring that the data warehouse is optimised for API's by collaborating with Software Engineers.