Description

You will design, build, and maintain data pipelines and data lake infrastructure.

Responsibilities

• Design, build, and maintain ETL/ELT data pipelines using AWS-native tools (Glue, Lambda, Step Functions, EMR).
• Develop scalable data ingestion solutions from structured and unstructured sources.
• Build data transformation workflows using PySpark, Spark, or PySpark, Spark-based frameworks.
• Manage large-scale AWS S3-based Data Lakes, implementing partitioning and cataloging via AWS Glue Data Catalog.
• Optimize Amazon Redshift data models and build high-performance ELT workloads using SQL, Spectrum, and COPY commands.

Required Skills

• 7+ years of hands-on experience in data engineering.
• Expertise with S3, Glue, Redshift, Lambda, IAM, CloudWatch, and EMR.
• Deep knowledge of Amazon Redshift, including optimization and performance tuning.
• Strong SQL skills and experience with data modeling (star/snowflake).
• Experience building and managing data lakes and large-scale data pipelines.
• Hands-on experience with PySpark/Spark or similar distributed processing frameworks.
• Experience with streaming technologies such as Kinesis or Kafka.
• Strong understanding of ETL/ELT architecture and data integration patterns.

Key Skills

Education

Any Gradute