Senior Data Engineer
Skills
About This Role
Overview
- As a Data Engineer within our AI practice, you will ensure our data workflows are efficient, our pipelines are reliable, and our systems are scalable.
- Your key responsibilities include:
- GenAI Optimization:
- Building and maintaining data lakes, warehouses, and feature stores specifically optimized for GenAI workloads.
- Pipeline Engineering:
- Designing and implementing robust ETL/ELT processes and automated workflows using tools like
Apache Airflow
or
Luigi
- .
- Architecture Design:
- Developing large-scale, high-performance data architectures that support real-time data processing and streaming.
- Cross-Functional Collaboration:
- Working side-by-side with Data Scientists and ML Engineers to understand feature engineering and data augmentation requirements.
- Innovation:
- Exploring and implementing emerging tools and technologies to enhance our evolving GenAI product development.
Technical Requirements
- Programming:
- Proficiency in
Python
is a must.
Familiarity with Scala or Java is highly valued.
• Big Data Frameworks
Hands-on experience with
Apache Spark
- or Hadoop.
- Database Mastery:
- Expert knowledge of SQL and NoSQL (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
- AI/ML Context:
- Solid understanding of data preparation for Machine Learning, including MLOps tools and practices (
MLflow, DVC
- ).
- Version Control:
Experience
managing codebases and documentation via
Git
or GitLab.
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