Role: Data Engineer 

Mode: Full Time

Location: NYC, NY - Onsite
Role Overview:
As a Data Engineer at Blockboard, you will be responsible for designing, building, and maintaining our data infrastructure, ensuring its scalability, reliability, and efficiency. You will work closely with cross-functional teams to understand data requirements, develop ETL pipelines, and implement solutions for data processing, storage, and analysis.

Key Responsibilities:

• Design, develop, and maintain ETL pipelines for processing large volumes of data from various sources, ensuring data quality and integrity.
• Implement and optimize data models and schemas using Delta Tables on Databricks platform.
• Develop and maintain data warehousing solutions, including data modeling, indexing, and optimization.
• Troubleshoot and resolve data-related issues, ensuring data pipelines are running smoothly and efficiently.
• Stay updated with the latest technologies and trends in data engineering and recommend innovative solutions to improve our data infrastructure.

Qualifications:

• Bachelor's degree or higher in Computer Science, Engineering, or related field.
• 5+ years of experience working as a Data Engineer or similar role.
• Strong proficiency in Python and Spark for scripting and programming.
• Hands-on experience with Databricks platform and Delta Tables for data processing and storage.
• Proficiency in SQL and experience with relational databases such as MySQL.
• Experience with cloud platforms, particularly AWS (Amazon Web Services), and services like S3, RDS, Glue, etc.
• Solid understanding of data warehousing concepts, ETL processes, and data modeling techniques.
• Excellent problem-solving skills and ability to work independently or in a team environment.
• Desired Skill set:
• Experience with streaming data processing frameworks such as Apache Kafka or Apache Spark Streaming.
• Familiarity with OpenAI / LLM models for natural language processing tasks.
• Experience with data visualization tools such as Tableau, Power BI, or similar.