Senior Data Engineer at RecruiTech

A great opportunity for a Senior Data Engineer (Hybrid) to join a growing Consulting company. You will be working with businesses using the latest technologies to provide data strategies at all levels of the organization.

Responsibilities

Data Strategy and Architecture: Collaborate with business stakeholders, Data Integration teams, Data Scientists, and other IT teams to understand data requirements and develop a cohesive data strategy and architecture that aligns with the organization's goals.
Data Modeling: Design and optimize data models, ensuring they meet data storage, retrieval, and analytical needs while maintaining data integrity and performance. Model data across business Entities and Normalization strategies.
Data Pipeline Development: Design and create data pipelines, which are a series of automated processes that extract data from various sources, transform it into a suitable format, and load it into data storage systems (e.g., databases, data warehouses, data lakes).
Data Integration: Ensure seamless integration of data from different sources, including databases, applications, APIs, and third-party platforms. This involves understanding the data schema, data formats, and data quality requirements to enable smooth data flow between systems.
Data Transformation: Transform raw data into a usable format by applying cleansing, enrichment, aggregation, and other data processing techniques. This step prepares the data for analysis and reporting by data analysts and data scientists.
Data Warehouse Management: Work with data warehousing technologies to design create and maintain data warehouses, which are centralized repositories of structured and organized data used for reporting and analysis.
Data Governance and Compliance: Establish data governance frameworks, data quality standards, and data security measures as well also set up appropriate access controls to safeguard sensitive data. Ensure compliance to fiscal, legislative and industry governance and standards.
Real-time Data Processing: Create and develop real-time data processing solutions that allow organizations to analyze and act on data as it is generated or received.
Data Quality Assurance: Implement data quality checks and validation processes to identify and resolve data inconsistencies or errors that may affect the accuracy of analytical results.
Data Landscapes: Work with a combination of cloud-based data platforms like Amazon Web Services (AWS), Microsoft Azure, as well as SaaS and On-premises environments, often using a combination of different landscapes.
Collaboration: Collaborate closely with data scientists, data analysts, business stakeholders, and other IT teams to understand data requirements and ensure the delivery of accurate and reliable data solutions.
Performance Optimization:Continuously monitor and optimize data processes, pipelines, and storage systems to improve performance, reduce latency, and lower operational costs.
Team Leadership and Collaboration: Responsible for providing technical leadership and mentorship to data engineering teams, fostering a collaborative and innovative work environment.
Project Management: Responsible for the design, management and implementation of related project schedules to ensure effective communication to stakeholders on project cost, schedules and compliance.
Emerging Technologies: Stay updated with the latest trends and advancements in data technologies, recommending and implementing innovative solutions that drive data innovation within the organization.
Documentation: Ultimately responsible for draughting the data integration blueprint. They document their data pipelines, architectures, and processes, enabling easier maintenance, troubleshooting, and knowledge sharing across the team.
Technical Support and Troubleshooting: Provide technical support to end-users, troubleshoot data-related issues, and assist with ad-hoc data requests and analyses, in addition to creating enablement structures designed to self-manage platforms.

Requirements

Diploma / Bachelor's Degree in Information Technology, Computer Science, Engineering, or similar degree with a major in Mathematics.
Vendor certifications in a Data Integration platform such as Talend, Attunity, Oracle, SQL or other ETL / Data Warehouse tool.
Proven experience (8 years) in data warehousing / systems design, development, and implementation across multiple business pillars.
Expertise in ETL tools and processes, data integration techniques, and data quality management.
Strong understanding of ERD's and Data Normalization / Denormalization techniques.
Working knowledge of Inmon and Kimball data modelling methods.
Proficiency in both SQL and non-SQL querying languages for data extraction and manipulation.
Strong knowledge of BI tools (e.g., Qlik, Power BI, Tableau) and visualization best practices.
In depth knowledge of database management systems (e.g., SQL Server, Oracle, MySQL) and data modeling concepts (e.g., star schema, snowflake schema).
Understanding of data security and privacy practices, e.g., POPIA or King
Experience with big data technologies and cloud-based data warehousing platforms (e.g., Azure)
Working knowledge of API / JSON / SOAP based connectivity.
Expansive knowledge and experience of Industry direction.

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Desired Skills