Senior Data Engineer

About the position

Design, build, and maintain ELT pipelines:           



Pipeline success rate (% jobs completed without failure).



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.



Data Lake / Warehouse Management:   



System uptime and availability.



Query performance (execution speed).



Storage usage vs. capacity.



Structured & Clean Data Sets:   



Data quality score (accuracy, completeness, validity).



Number of reported data issues.



Resolution turnaround time.



Collaboration with BI & Data Science Teams:     



Time to deliver datasets for reports/models.



Internal stakeholder satisfaction rating.



Support requests resolved within SLA.



Documentation & Data Cataloguing:      



% of pipelines with up-to-date documentation.



Metadata completeness.



User ease of data discovery.



Security & Compliance Support:



% compliance with access control policies.



Number of unauthorised access incidents.



Audit readiness/ completion rate.



Process Automation:



Manual hours reduced via automation.



Number of recurring tasks automated.



Stability of automated workflows.



Issue Analysis & Root Cause Investigations:



Time to issue resolution (from investigation to recommendation).



Number of root causes correctly identified.



Reduction in repeated issues.


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.

Desired Skills:

• Data warehousing concepts
• Data integration
• Apache Airflow