While both positions are vital to a modern tech organisation, MLOps Engineer and BI Developer have fundamentally different daily workflows.
MLOps Engineer focuses primarily on building and maintaining ML training and inference pipelines, model deployment workflows, and monitoring infrastructure using MLflow, Kubeflow, and Azure ML to productionise machine learning at scale. Day-to-day work revolves around designing and maintaining ML training and inference pipelines, building CI/CD workflows for model deployment with MLflow, Kubeflow, and Azure ML, monitoring model performance and data drift in production, collaborating with data scientists to productionise experimental models, managing containerised ML workloads on Kubernetes, provisioning and optimising GPU infrastructure, and supporting model governance and auditability requirements.
BI Developer focuses on designing and building Power BI, Tableau, and Looker dashboards, dimensional data models, and ETL pipelines that turn raw data into actionable business insights. Their time is spent building Power BI and Tableau dashboards, writing SQL queries and stored procedures, designing star and snowflake schema data models, developing ETL and ELT pipelines with SSIS or dbt, gathering reporting requirements from business stakeholders, maintaining data warehouses, and optimising query performance.
