Vertical Relevance

AWS Data Cloud Consultant

Vertical Relevance

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Mid-LevelSenior

Tech Stack

AirflowAmazon RedshiftApacheAWSCloudDynamoDBHadoopKafkaPythonSparkTerraform

About the role

  • End-to-end planning, building, and deploying of software systems and data solutions on AWS for customer engagements
  • Drive programming of well-constructed, testable code and be a deep technical resource for customers
  • Help customers shape their journey to adopting the cloud and provide technical and strategic guidance on their cloud journey
  • Consult, plan, design, and implement Data solutions on the cloud customers, including AWS Data Lake implementations and Data Quality/Governance solutions
  • Develop high-quality technical content such as automation tools, reference architectures, and white papers
  • Innovate on behalf of customers and translate ideas into measurable results; convey customer needs and feedback into technology roadmaps
  • Assist with technical briefs and reference architecture implementations
  • Support internal and external brand development through thought leadership (work with Marketing/Alliances on blog posts and case studies)
  • Assist customers with building frameworks for data collection, ingestion, cataloging, storage, and serving, including S3 bucket strategies, encryption, partitioning, lifecycle, and cross-region replication
  • Build reusable ingestion frameworks, serverless ingestion where applicable, and orchestration/state-management for lambda architectures
  • Advise customers on AWS service choices for ingestion, orchestration, and analytics and implement monitoring, logging, and alerting strategies to avoid data loss and handle exceptions

Requirements

  • Professional experience architecting/operating Data / DevOps solutions built on AWS
  • Experience in software/technology customer facing experience
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
  • Familiarity with data lake design and implementation, data quality and governance
  • Experience building data ingestion, transformation, orchestration, and serving layers
  • Experience with Amazon VPC, S3 bucket strategy, and lambda/big-data architectures
  • Experience implementing monitoring, logging, alerting, retry and error-handling mechanisms for data pipelines
  • Must Have: Apache Iceberg, SageMaker, SageMaker Lakehouse, SageMaker Catalog, Terraform
  • Should Have: Athena, Redshift, EMR, Glue, DataBricks and/or Snowflake
  • Primary language: Python
  • Tooling/Services/Libraries: Airflow, Kafka, Parquet, Spark, Metaflow, Git, Hadoop
  • AWS tooling: CloudFormation, AWS CLI, AWS CDK, IAM, CloudTrail, Service Catalog, Step Functions, Data Pipeline
  • Experience advising on and selecting AWS analytics and storage services (S3, Lambda, EMR, Kinesis, Glue, Lake Formation)
  • Mid Level (as listed in posting)