Data Engineer (ML Pipeline Focus)

Job Description

DTG Global is working with a high-growth healthtech company using advanced machine learning to personalise healthcare insights at scale. They are looking for a Data Engineer with a focus on building robust pipelines for machine learning workflows.

You’ll be working at the intersection of data engineering and MLOps, ensuring clean, reliable data pipelines are in place to support model training, deployment and monitoring in production.

What you’ll be doing

• Designing and maintaining ETL/ELT pipelines to feed machine learning models with high-quality, timely data
• Automating data workflows using orchestration tools (Airflow, Prefect)
• Collaborating with data scientists to productionise feature engineering processes
• Supporting the integration of pipelines with model training and deployment workflows
• Managing and optimising data infrastructure in a cloud environment (AWS/GCP)

What we’re looking for

• Strong Python programming skills with experience in building scalable data pipelines
• Proficiency with SQL and working with relational and non-relational data stores
• Familiarity with workflow orchestration tools (Airflow, Prefect, Dagster)
• Experience with cloud data services (S3, BigQuery, Redshift, Snowflake)
• Understanding of CI/CD practices within data engineering workflows

Nice to have

• Exposure to MLOps tooling and practices (MLflow, Kubeflow, SageMaker)
• Experience with streaming data frameworks (Kafka, Spark Streaming)
• Familiarity with infrastructure-as-code (Terraform)
• Interest in working closely with machine learning teams to bridge data engineering and MLOps

Why this role

• Build pipelines that directly power meaningful machine learning applications
• Join a mission-led healthtech company with a strong technical culture
• Flexible working with the option of fully remote or hybrid
• Competitive salary, benefits and a performance-based bonus

If you’re looking to step into a role where your data engineering skills enable machine learning models to make a real-world impact, we’d love to hear from you.

Apply now or contact us for a confidential discussion.