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 AWS services like Lambda, Glue, Redshift, S3, etc., to process, analyze, and store large-scale datasets efficiently.
  

• Develop and optimize data pipelines using tools such as AWS Glue for ETL tasks, PySpark for big data processing, and Python for scripting and automation. Additionally, experience with Apache Spark/Databricks is highly desirable for advanced ETL workflows.
  

• Write and maintain SQL queries for data retrieval, transformation, and storage in relational databases like Redshift or PostgreSQL.
  

• Collaborate with cross-functional teams, including data scientists, 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:

• 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.
  

If you have what it takes to join our team as a Data Engineer, we encourage you to apply.