Data Engineer

Job Description:

As a Data Engineer for our Large Language Model Project, you will play a crucial role in designing, implementing, and maintaining the data infrastructure required for the development and deployment of advanced natural language processing models. Your expertise will be instrumental in ensuring the efficient flow of data, enabling seamless integration with various components, and optimizing data processing pipelines.

Key Responsibilities:

• Data Pipeline Development - Design, develop, and maintain scalable and efficient data pipelines to support the training and deployment of large language models. Implement ETL processes to extract, transform, and load diverse datasets into suitable formats for model training.
• Data Integration - Collaborate with cross-functional teams, including data scientists and software engineers, to integrate data sources and ensure the availability of relevant and high-quality data. Implement solutions for real-time data processing and integration.
• Data Quality Assurance - Establish and maintain robust data quality checks and validation processes to ensure the accuracy and consistency of datasets. Troubleshoot data quality issues, identify root causes, and implement corrective measures.
• Infrastructure Management - Work closely with DevOps and IT teams to manage and optimize the data storage infrastructure, ensuring scalability and performance. Implement best practices for data security, access control, and compliance with data governance policies.
• Performance Optimization - Identify bottlenecks and inefficiencies in data processing pipelines and implement optimizations to enhance overall system performance. Continuously monitor and evaluate system performance metrics, making proactive adjustments as needed.

Skills & Tools

• Database Systems - In-depth knowledge of both relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra etc).
• Data Warehousing - Familiarity with data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake.
• ETL Tools - Hands-on experience with ETL tools like Apache NiFi, Talend, or Apache Airflow.
• Cloud Services - Experience with cloud platforms like AWS, Azure, or Google Cloud for deploying and managing data infrastructure.
• Programming Languages - Proficiency in languages such as Python and SQL for building robust data processing applications.
• Problem Solving - Analytical mindset with a proactive approach to identifying and solving complex data engineering challenges.