Description

You will design, develop, and implement data models for enterprise-level applications while managing end-to-end ETL pipelines.

Responsibilities

• Design and deploy serverless data pipelines using AWS Lambda and Glue Catalog.
• Execute cloud migrations from on-premises environments to AWS and Azure.
• Build and manage reporting and analytics infrastructure for internal business clients.
• Establish and maintain multi-node Hadoop clusters and optimize algorithms using Spark.
• Connect Azure to on-premises data centers via Azure Express Route.

Required Skills

• 8+ years of experience in Data Engineering, ETL Development, or Software Engineering.
• Extensive AWS expertise including S3, EMR, Redshift, DynamoDB, Athena, Glue, Kinesis, Lambda, and EC2.
• Proficiency in Azure Cloud services including Data Factory, Data Lake Storage, Synapse Analytics, and Azure SQL.
• Advanced Python programming with Object-Oriented principles and libraries like NumPy, Pandas, SciPy, and Matplotlib.
• Experience with Big Data technologies including Apache Spark (Spark-SQL, DataFrames, RDD) and Hadoop (HDFS, MapReduce).
• Hands-on experience with Snowflake and SnowSQL.
• Infrastructure as Code and CI/CD experience using Terraform, Chef, Jenkins, GitHub, and Docker.
• Ability to develop JSON-based RESTful and XML-based SOAP web services.

Preferred Skills

• Experience with Databricks.

Key Skills

Education

Any graduate