• Production experience with Spark at scale
• Building data pipelines, CICD pipelines, and fit for purpose data stores
• Dimensional Data Modeling
• Building data pipelines that process more than 1TB both in streaming and batch mode
• Working with data consumption patterns
• Working with automated build and continuous integration systems
• Microservices development in two of these languages: Python, Java, Scala
• Big Data Technologies: Apache Spark, Hadoop
• Relational Databases: Postgres, MySQL
• NoSQL: MongoDB, DynamoDB
• Data-warehousing products: Snowflake or Redshift
• Cloud technologies: AWS (Terraform, S3, EMR, EC2, Glue, Athena)
• Orchestration: Apache Airflow and MLFlow
Nice to have:
• Spark on Kubernetes
We need a hero:
• someone who has played a role at a Lead /arch level and has been involved with dealing with data at scale in PROD
• who is able to analyze, assess, document, and design scalable and sustainable data architecture and data transformation processes within a large analytics pipeline
• who will review the implementation of improvements to the data architecture, providing feedback and guidance to product developers, data scientists, and product owners
• is comfortable collaborating with various teams/regions in driving facilitating data design, identifying architectural risks, and developing and refining data models and architecture frameworks.
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