Role summary
Build trusted data pipelines and quality controls supporting analytics, risk and applied-AI use cases.
What you will do
- Develop batch and streaming pipelines
- Improve data quality, lineage and observability
- Partner with analysts and ML teams
- Optimise reliability and cost
What you need
- Strong SQL and Python
- Data-pipeline experience
- Cloud data-stack familiarity
- Understanding of data modelling
Preferred experience
- Spark or distributed processing
- Data governance
- ML feature-pipeline exposure
What the employer offers
- Hybrid work
- Learning budget
- Health coverage
- Data platform ownership
Interview process
- 01
Profile review
- 02
Data engineering round
- 03
Pipeline case
- 04
Team round
- 05
Decision
