Data Engineer

Role Summary:

highly skilled and proactive Data Engineer

Key Responsibilities:

• Architect, develop, and maintain

  high-performance data pipelines

   using AWS services such as Glue, Lambda, EMR, ECS, and Step Functions.
• Design and implement

  streaming data solutions using Apache

  Kafka

  , ensuring reliability, scalability, and fault tolerance.
• Build and optimize

  ETL/ELT workflows

   for large-scale structured and semi-structured datasets.
• Write clean, efficient, and reusable

  Python and PySpark code

   for data transformation and analytics.
• Collaborate with cross-functional teams including

  Architecture, Business Analysts, and Application Support

   to deliver end-to-end data solutions.
• Monitor and enhance

  cloud infrastructure performance

   using logs, metrics, and alerts.
• Troubleshoot and resolve

  pipeline failures, data quality issues, and system errors

  .
• Work closely with the Application Support team to implement

  automated monitoring and alerting solutions

  .
• Leverage

  Snowflake

   for data warehousing and analytics, ensuring optimal query performance and cost efficiency.
• Analyze, write, and optimize complex

  SQL queries

   for data extraction, reporting, and dashboarding.

Required Qualifications:

• 5+ years

   of hands-on experience in data engineering and analytics.
• Proven expertise in

  AWS cloud services

  , especially Glue, Lambda, EMR, ECS, and Step Functions.
• Strong proficiency in

  Apache

  Kafka

   for real-time data streaming and event-driven architectures.
• Advanced skills in

  Python

   and

  PySpark

   for big data processing.
• Deep understanding of

  Snowflake

   architecture, performance tuning, and data modeling.