Data Engineer

Key Responsibilities:

• Design, develop, and maintain ETL (Extract, Transform, Load) processes to ensure the seamless integration of raw data from various sources into our data lakes or warehouses.
• Utilize Python, PySpark, SQL and AirFlow etc., to process, analyze, and store large-scale datasets efficiently.
• Write and maintain SQL queries for data retrieval, transformation, and storage in relational databases like Redshift or PostgreSQL.
• Support cloud-based data platforms such as AWS, Azure, or GCP, with a focus on orchestrating AI retraining cycles, versioning, and automated pipeline monitoring.
• Familiarity in converting unstructured data into vectors using frameworks like LangChain or LlamaIndex and storing them.
• Collaborate with cross-functional teams, including data scientists, ML engineers, and domain experts to design and implement scalable solutions.
• Troubleshoot and resolve performance issues, data quality problems, and errors in data pipelines.
• Document processes, code, and best practices for future reference and team training.

Additional Information:

• Experience level 3+ years.
• Strong understanding of data governance, security, and compliance principles is preferred.
• Ability to work independently and as part of a team in a fast-paced environment.
• Excellent problem-solving skills with the ability to identify inefficiencies and propose solutions.
• Experience with version control systems (e.g., Git) and scripting languages for automation tasks.