Required qualifications, capabilities, and skills
- Formal training or certification on data engineering concepts and 2+ years applied experience
- Experience in software development and data engineering, with demonstrable hands-on experience in Python and PySpark.
- Proven experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Good understanding of data modeling, data architecture, ETL processes, and data warehousing concepts.
- Experience with big data technologies and services like AWS EMRs, Redshift, Lambda, S3.
- Excellent communication skills to work effectively with stakeholders, partner teams, and to translate technical concepts into business terms. Proven experience with efficient Cloud DevOps practices and CI/CD tools like Jenkins/Gitlab, for data engineering platforms.
- Good knowledge of SQL and NoSQL databases, including performance tuning and optimization.
- Strong analytical skills to troubleshoot issues and optimize data processes, working independently and collaboratively.
- Experience in working within a team of engineers, with a proven track record of successful project support.
Preferred qualifications, capabilities, and skills
- Knowledge of machine learning concepts, language models, and cloud-native MLOps pipelines and frameworks is a big plus.
- AWS Certifications in data engineering and machine learning is a plus.
- Familiarity with data visualization tools and data integrations.