Data Engineer
Skills
About This Role
Overview
We are looking for an experienced Data Engineer to join our team full-time.
This role requires a strong background in building and optimizing data pipelines, with a focus on supporting AI-driven solutions.
The candidate will work closely with software engineers to ensure reliable data flows for AI models and contribute to cloud-based data integration using Microsoft Azure.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines and ETL processes to support AI-driven projects
- Build and optimize data infrastructure to ensure efficient data management, processing, and storage in cloud environments (Azure)
- Collaborate with AI engineers to facilitate data preparation for machine learning models and AI solutions
- Ensure data pipelines are secure, reliable, and meet performance standards
- Monitor and troubleshoot data pipeline performance to ensure timely and accurate data delivery
- Implement data governance policies and ensure compliance with data security standards
Required Qualifications
- Proven experience as a Data Engineer, with a focus on building and maintaining complex data pipelines for AI projects
- Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, SQL Server) and NoSQL databases (e.g., MongoDB)
- Expertise in cloud-based data solutions, specifically Microsoft Azure (e.g., Azure Data Factory, Azure Synapse, Azure Databricks)
- Experience with data integration tools and ETL frameworks (e.g., Apache Airflow)
- Proficiency in at least one programming language for data manipulation (e.g., Python)
- Knowledge of data warehousing solutions and best practices
- Experience in working with data pipelines for AI or machine learning workflows
- Strong problem-solving and analytical skills
- Bachelor’s degree in Computer Science, Engineering, or a related field
- Proficiency in English (written and spoken)
Desirable Qualifications
- Familiarity with machine learning models and data preprocessing for AI solutions
- Knowledge of containerization tools (Docker, Kubernetes) for data pipeline deployments
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