About the position
- Design, build, and maintain ELT pipelines
- Data latency (extraction to availability)
- Error resolution turnaround time
- 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
- System uptime and availability
- Query performance (execution speed)
- Data quality score (accuracy, completeness, validity)
- Number of reported data issues
- Time to deliver datasets for reports/models
- Internal stakeholder satisfaction rating
- Compliance with access control policies
- Number of unauthorised access incidents
- Audit readiness/ completion rate
- Manual hours reduced via automation
- Number of recurring tasks automated
- Stability of automated workflows
- Time to issue resolution (from investigation to recommendation)
- Number of root causes correctly identified
- 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
Minimum Requirements:
Minimum Requirements (Experience & Qualifications)
Bachelor's/Masters in Computer Science or related field
Preferred certifications:
- Microsoft Certified: Azure Data Engineer Associate
- Google Professional Data Engineer
- AWS Certified Data Analytics
- 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
Required Knowledge:
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
Required Skills:
- 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
- Working knowledge of cloud-based data platforms (e.g. Azure, AWS, GCP)
Desired Skills:
- SQL and Python
- Data validation
- Cloud-based data platforms
