Data Engineer – AI & Analytics Platform
| Dyddiad hysbysebu: | 10 Chwefror 2026 |
|---|---|
| Oriau: | Llawn Amser |
| Dyddiad cau: | 06 Mawrth 2026 |
| Lleoliad: | Ilford, Essex |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 2 ddiwrnod yr wythnos |
| Cwmni: | Datashrubs Technologies Ltd |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: |
Crynodeb
We are looking for a Data Engineer to build and scale the data infrastructure, pipelines, and AI-ready architecture powering our global engineering marketplace, revenue intelligence, and product analytics.
This role ensures accurate GMV/revenue data, real-time analytics, and ML/NLP-ready datasets for decision-making and investor reporting.
Key Responsibilities
Design and maintain scalable ETL/ELT pipelines using Python and SQL
Build data warehouse models for revenue, users, and product analytics
Enable ML/NLP workflows with clean, feature-ready datasets
Deliver trusted data to BI dashboards and optimize performance
Implement data quality, security, and governance controls
Required Experience
5+ years in Data Engineering or Analytics Engineering
Strong record of building production-grade pipelines and warehouses
Technical Skills
Python, SQL, data modeling
Airflow/dbt, modern data warehouses (Snowflake/BigQuery/Redshift)
AWS/GCP/Azure cloud environments
Exposure to ML/NLP data preparation and BI tools (Power BI/Tableau/Looker)
This role ensures accurate GMV/revenue data, real-time analytics, and ML/NLP-ready datasets for decision-making and investor reporting.
Key Responsibilities
Design and maintain scalable ETL/ELT pipelines using Python and SQL
Build data warehouse models for revenue, users, and product analytics
Enable ML/NLP workflows with clean, feature-ready datasets
Deliver trusted data to BI dashboards and optimize performance
Implement data quality, security, and governance controls
Required Experience
5+ years in Data Engineering or Analytics Engineering
Strong record of building production-grade pipelines and warehouses
Technical Skills
Python, SQL, data modeling
Airflow/dbt, modern data warehouses (Snowflake/BigQuery/Redshift)
AWS/GCP/Azure cloud environments
Exposure to ML/NLP data preparation and BI tools (Power BI/Tableau/Looker)