Key skills
AirflowAWSCloudETLPythonMachine LearningMLGlueSageMakerAgileMentoringCommunication
About this role
Role Overview
- Building and maintaining data and machine learning pipelines in production
- Developing ETL and data processing workflows using Python
- Orchestrating workflows using tools such as Airflow and AWS Step Functions
- Working with AWS Glue for data processing and pipeline execution
- Supporting the deployment of machine learning models into production using SageMaker
- Working closely with Data Scientists and ML Engineers to productionise ML models
- Monitoring, troubleshooting, and improving pipeline reliability and performance
- Contributing to architecture and technical decisions around data platform and ML infrastructure
- Working in an agile environment with distributed teams
Requirements
- Strong experience with Python
- Experience building data pipelines and/or ML pipelines in production
- Hands-on experience with Airflow
- Experience with AWS Step Functions and AWS Glue
- Experience deploying ML models using AWS SageMaker
- Strong experience with AWS cloud
- Experience working with production systems at scale
- Experience working in cross-functional teams with Data Scientists
- Strong communication skills and ability to work in a distributed team environment.
- Strong English communication skills, both written and verbal
Tech Stack
- Airflow
- AWS
- Cloud
- ETL
- Python
Benefits
- A culture in which we don’t criticise failure but ensure we learn from our mistakes
- An Agile environment where your ideas are welcome
- Ongoing Training & Mentoring
- The possibility of travel