About this role
Data Engineer – ML/AI Ops & Cloud Data Platforms Location: Hybrid, Lagos State Salary: NGN 1M – 1.5M Net Monthly
A professional data engineering role focused on ML/AI operations and cloud data platforms. The position supports building and operating scalable data pipelines and platform capabilities to enable ML-driven insights and decisioning. You will work in a hybrid environment in Lagos State and contribute to data engineering initiatives across cloud data platforms and ML/AI workflows.
Responsibilities
- Design, develop, and maintain production-grade ETL/ELT pipelines on AWS, Azure, or GCP.
- Build and optimize data platforms and data lake/warehouse solutions to support ML/AI workloads.
- Collaborate on ML/AI operations (MLOps) practices to streamline model deployment, monitoring, and lifecycle management.
- Work with Databricks or Snowflake as core data platforms, including implementation and optimization tasks.
- Implement and manage infrastructure as code and cloud-native tooling, including Terraform.
- Integrate streaming architectures and participate in fraud detection data flows as needed.
- Engage with financial services or fintech data contexts, including Nigerian financial data, Open Banking, or core banking integrations when relevant.
- Maintain data quality, governance, and best practices across cloud data engineering initiatives.
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Mathematics, or a related field.
- 4+ years of data engineering experience, including 2+ years with Databricks or Snowflake.
- Strong hands-on expertise in Python, SQL, and PySpark.
- Experience building production-grade ETL/ELT pipelines on AWS, Azure, or GCP.
- Knowledge of Terraform, MLOps, and cloud data engineering best practices.
- Experience in financial services or fintech is highly desirable.
- Databricks or Snowflake certifications are a plus.
- Experience with Nigerian financial data, Open Banking, or core banking integrations is advantageous.
- Knowledge of streaming architectures, fraud detection, and LLMOps is desirable.