Data Engineer (Data Science & Automation)

Lagos - Nigeria

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

Design develop and maintain scalable data models and pipelines using SQL and Google BigQuery
Support the MLOps lifecycle including model versioning monitoring for data drift and automated retraining loops.
Manage data warehousing best practices within the Google Cloud Platform (GCP) environment to ensure scalability and cost-efficiency
Maintain CI/CD pipelines to ensure seamless deployments across development and production environments
Extract clean and transform large datasets from multiple sources within the Google Cloud Platform (GCP) ecosystem
Build automated data workflows for advanced data manipulation statistical analysis and reporting systems using Python-based automation
Build automated data pipelines to eliminate manual data handling and ensure real-time insight delivery.
Support the execution of end-to-end data science projects: including data cleaning feature engineering machine learning model selection and post-deployment monitoring
Collaborate with cross-functional teams (Analytics Product Engineering Finance Marketing) to define data requirements.
Ensure data quality integrity and governance across all analytics processes.

Bachelors or Masters degree in Data Science Statistics Computer Science Mathematics or related field
Minimum of 5 years of experience in data analytics or a similar role.
Strong proficiency in SQL (complex queries optimization data modelling)
Hands-on experience with Google BigQuery and the GCP ecosystem
Advanced proficiency in Python for data analytics and automation
Familiarity with ML libraries like Scikit-learn TensorFlow or PyTorch
Solid understanding of data warehousing concepts and ETL processes
Knowledge of cloud-based data pipelines and orchestration tools (e.g. Airflow)
Familiarity with version control systems (e.g. Git)
Experience working in Agile environments
Exposure to data governance and security best practices
Attention to detail and data accuracy
Business acumen and strategic thinking
Strong communication and stakeholder management skills
Ability to work independently and lead initiatives

Required Experience:

Design develop and maintain scalable data models and pipelines using SQL and Google BigQuerySupport the MLOps lifecycle including model versioning monitoring for data drift and automated retraining loops.Manage data warehousing best practices within the Google Cloud Platform (GCP) environment to ens...

Design develop and maintain scalable data models and pipelines using SQL and Google BigQuery
Support the MLOps lifecycle including model versioning monitoring for data drift and automated retraining loops.
Manage data warehousing best practices within the Google Cloud Platform (GCP) environment to ensure scalability and cost-efficiency
Maintain CI/CD pipelines to ensure seamless deployments across development and production environments
Extract clean and transform large datasets from multiple sources within the Google Cloud Platform (GCP) ecosystem
Build automated data workflows for advanced data manipulation statistical analysis and reporting systems using Python-based automation
Build automated data pipelines to eliminate manual data handling and ensure real-time insight delivery.
Support the execution of end-to-end data science projects: including data cleaning feature engineering machine learning model selection and post-deployment monitoring
Collaborate with cross-functional teams (Analytics Product Engineering Finance Marketing) to define data requirements.
Ensure data quality integrity and governance across all analytics processes.

Bachelors or Masters degree in Data Science Statistics Computer Science Mathematics or related field
Minimum of 5 years of experience in data analytics or a similar role.
Strong proficiency in SQL (complex queries optimization data modelling)
Hands-on experience with Google BigQuery and the GCP ecosystem
Advanced proficiency in Python for data analytics and automation
Familiarity with ML libraries like Scikit-learn TensorFlow or PyTorch
Solid understanding of data warehousing concepts and ETL processes
Knowledge of cloud-based data pipelines and orchestration tools (e.g. Airflow)
Familiarity with version control systems (e.g. Git)
Experience working in Agile environments
Exposure to data governance and security best practices
Attention to detail and data accuracy
Business acumen and strategic thinking
Strong communication and stakeholder management skills
Ability to work independently and lead initiatives

Required Experience: