AI Data Engineer
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Key skills for this role
Role Overview
The employer is seeking a hands-on AI Data Engineer to build and optimize scalable data infrastructure and pipelines for AI and analytics initiatives.
The role works closely with Software Engineering and AI/ML teams across structured and unstructured datasets.
Key Skills for This Role
Full Job Posting
About the Role
The employer is seeking a hands-on AI Data Engineer to build and optimize scalable data infrastructure and pipelines for AI and analytics initiatives.
The role works closely with Software Engineering and AI/ML teams across structured and unstructured datasets.
Key Responsibilities
- Design, develop, and maintain robust ETL/ELT pipelines across multiple data sources.
- Build scalable workflows using Airflow, dbt, or equivalent orchestration tools.
- Process structured and unstructured datasets for AI, ML, and analytics use cases.
- Implement validation, testing, monitoring, and data quality frameworks.
- Diagnose data issues and improve pipeline reliability and performance.
- Collaborate with AI/ML and Software Engineering teams to provide reliable, accessible data.
- Apply data governance, security, quality, and lineage best practices.
- Document architectures, metadata, and data flows for transparency and reproducibility.
- Optimize pipelines and infrastructure for scalability, performance, and cost efficiency.
Requirements
- At least 5 years of experience in data engineering, ETL development, or data warehousing.
- Strong hands-on experience with Python and SQL.
- Experience with Airflow, dbt, Luigi, or similar workflow orchestration technologies.
- Strong understanding of big data architectures and scalable data pipelines.
- Experience with large-scale structured and unstructured datasets.
- Experience with at least one major cloud platform: Azure, AWS, or GCP.
- Strong understanding of data quality, validation, monitoring, and governance.
- Experience diagnosing complex data and pipeline issues.
- Strong communication skills with technical and non-technical stakeholders.
- Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
Advantageous Experience
- Experience with Spark, data lakes or lakehouses, streaming, vector databases, RAG pipelines, or AI data platforms is highly advantageous.
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