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Astra North Infoteck Inc.

11 months ago

"AWS Data Engineer - Hadoop, Spark, Scala, AWS DataLake *Netezza"

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Company information

Company
Astra North Infoteck Inc.
Location
Canada, Ontario, Toronto Canada
Posted
11 months ago
View all jobs at Astra North Infoteck Inc.

Job description

AWS Data Engineer - Hadoop, Spark, Scala, AWS DataLake *Netezza

Role and Responsibilities
  • Understand business requirements from product owners and convert them into technical scope and requirement documents

  • Design end-to-end data ingestion and transformation solutions using Hadoop ecosystem (Spark, Spark Streaming, Hive, etc.)

  • Create technical design documentation and mentor team members on implementation

  • Develop scalable solutions using Spark on AWS Data Lake

  • Build reusable frameworks for data engineering on AWS using services like S3, EMR, Glue, etc.

  • Coordinate with cross-functional teams (upstream/downstream) for production deployments

  • Provide post-production support and bug fixes as needed

  • Interpret and migrate existing Netezza/Hadoop features into AWS Data Lake architecture

  • Assist QA/SIT teams with unit testing, functional testing, and migration activities

  • Work with stakeholders to define reusable design patterns for data onboarding and integration


✅ 5 MUST-HAVE Skills & Experience
  1. Strong hands-on experience with Hadoop ecosystem, particularly Hive and Spark with Scala

  2. Extensive experience in data engineering on AWS, including S3, EMR, Glue, Redshift, Lake Formation, and Python

  3. Proficiency in PySpark and building batch workloads on Hadoop and AWS platforms

  4. Experience with code versioning and deployment tools like Bitbucket, Artifactory, and AWS CodePipeline

  5. Understanding and implementation of data encryption techniques and secure data handling


5 NICE-TO-HAVE Skills & Experience
  1. Experience handling terabyte/petabyte-scale data and processing millions of transactions per day

  2. Building and orchestrating ETL pipelines using Apache Airflow

  3. Knowledge of Spark Streaming or similar streaming technologies

  4. Proficiency in Scala or Java, and comfort with Linux-based environments

  5. Familiarity with AWS services like Secrets Manager, KMS, Lambda, and Pythonic pipeline design principles


Experience Required
  • 6+ years of hands-on experience in data engineering, data warehouse, and data lake platforms



Required skills

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