AWS - DATA ENGINEER {FACE TO FACE IS REQUIRED}

Interview Mode: MS Teams and In-person

Responsibilities:

• Design, build, and optimize scalable data pipelines to support business operations and analytics.
• Work extensively with AWS technologies such as S3, Lambda, Aurora DB, Glue, Kinesis, and Redshift for data processing and storage.
• Manage and enhance data workflows to streamline data ingestion, transformation, and storage.
• Develop and maintain ETL/ELT processes to ensure data accuracy, consistency, and security.
• Work with Enterprise Payment Systems to ensure seamless data integration and transaction processing.
• Design and optimize Aurora DB to support high-performance transactional workloads.
• Implement real-time data streaming solutions using AWS Kinesis or Kafka.
• Conduct deep-dive analysis on large datasets to drive strategic business insights.
• Collaborate with Data Scientists, Analysts, and Business Teams to develop data-driven solutions.
• Ensure data governance, security, and compliance best practices.
• Automate and optimize data operations for improved efficiency and scalability.

Required Skills:

• Bachelor s/Master s degree in Computer Science, Data Engineering, or a related field.
• 8+ years of hands-on experience in data engineering or related fields.
• Strong experience with AWS services (S3, Lambda, Glue, Aurora DB, Kinesis, Redshift, EMR, etc.).
• Proficiency in SQL, Python, or Scala for data manipulation and pipeline development.
• Experience working with relational and NoSQL databases, particularly AWS Aurora DB, PostgreSQL, or DynamoDB.
• Expertise in building and managing data pipelines for batch and real-time data processing.
• Hands-on experience with big data frameworks such as Spark, Hadoop, or Flink.
• Experience in Enterprise Payment Systems and handling large financial datasets is a plus.
• Strong analytical mindset with the ability to deep dive into data and generate meaningful insights.
• Proficiency in working with data modeling, data warehousing, and data architecture best practices.
• Familiarity with CI/CD pipelines, Infrastructure as Code (IaC), and DevOps practices for data workflows.
• Excellent problem-solving skills and ability to work in a fast-paced environment.