Job Description

To design, build, and maintain robust, scalable data infrastructure that ensures secure, real-time access to operational and supply chain data. This role underpins accurate reporting, advanced analytics, and automation--enabling performance optimisation, cost reduction, and smarter decision-making across the logistics value chain
1. Design, build, and maintain ELT pipelines

• Pipeline success rate

• Data latency (extraction to availability)

• Error resolution turnaround time

2. Integrate data across systems (WMS, TMS, ERP, IoT)

• Number of successful system integrations

• % data completeness and consistency across sources

• Average time to onboard new data source

3. Data Lake / Warehouse Management

• System uptime and availability

• Query performance (execution speed)

• Storage usage vs. capacity

4. Structured & Clean Data Sets

• Data quality score (accuracy, completeness, validity)

• Number of reported data issues

• Resolution turnaround time

5. Collaboration with BI & Data Science Teams

• Time to deliver datasets for reports/models

• Internal stakeholder satisfaction rating

• S% support requests resolved within SLA

6. Documentation & Data Cataloguing

• % of pipelines with up-to-date documentation

• Metadata completeness

• User ease of data discovery

7. Security & Compliance Support

• % compliance with access control policies

• Number of unauthorised access incidents

• Audit readiness/ completion rate

8. Process Automation

• Manual hours reduced via automation

• Number of recurring tasks automated

• Stability of automated workflows

9. Issue Analysis & Root Cause Investigations

• Time to issue resolution (from investigation to recommendation)

• Number of root causes correctly identified

• Reduction in repeated issues

10. Advanced SQL Development, Optimization & Data Engineering
Query performance improvements (execution speed, efficiency)
SQL-based data validation, transformation, and cleansing coverage
Reusable SQL pipelines for recurring logistics workflows (inventory,
shipments, routing)
SQL-based reconciliation across multiple systems
Version-controlled, documented SQL scripts aligned with governance standards
Reduction in errors or rework due to SQL inefficiencies
Education and Experience

• Bachelor''s/Masters in Computer Science or related field

• Preferred certifications:

o Microsoft Certified: Azure Data Engineer Associate
o Google Professional Data Engineer
o AWS Certified Data Analytics
o Certifications in BI or analytics tools (e.g. Power BI, Tableau, SQL)

• 3-5 years' experience in data engineering, preferably in logistics, supply chain sectors

Advanced proficiency in SQL and Python or another scripting language
Strong debugging, problem-solving, and performance tuning skills
Data validation, cleansing, and transformation techniques
Building scalable and reusable data pipelines
Communication skills
Development of ELT/ETL pipelines using tools like Apache Airflow, SSIS, or Azure Data Factory
Data integration across logistics systems (ERP, WMS, TMS, IoT
Data modelling, schema design, and SQL optimisation
Data warehousing concepts (e.g. star/snowflake schemas)
Version control and CI/CD pipelines for data products
Supply chain/logistics data structures and flows
Working knowledge of cloud-based data platforms (e.g. Azure, AWS, GCP)
Salary - R60 - R70 kpm CTC
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Skills Required