Google Cloud Platform Data Engineer

  • Salt Lake City, UT
  • Posted 2 days ago | Updated 2 days ago

Overview

On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)
100% Travel

Skills

Apache Kafka
Apache Spark
BMC Control-M
Banking
Cloud Computing
Collaboration
Continuous Delivery
Design Patterns
Documentation
Kubernetes
Data Lake
Data Structure
Good Clinical Practice
Docker
Durable Skills
Databricks
SQL
Linux
Kanban
Google Cloud Platform
SAFE
PySpark
Python
Microsoft Azure
Agile

Job Details

Title: Google Cloud Platform Data Engineer

Location: Salt Lake City, UT Onsite job

Experience: 10+ years only

Domain: Banking domain preferred

Actively migrate use cases from our on-premises Data Lake to Databricks on Google Cloud Platform

Collaborate with Product Management and business partners to understand case requirements and reporting
Adhere to internal development best practices/lifecycle (e.g. Testing, Code Reviews, CI/CD, Documentation)
Document and showcase feature designs/workflows
Participate in team meetings and discussions around product development
Stay up to date on industry, the latest trends and design patterns

Required qualifications to be successful in this role:

What you'll bring

  • 5+ years of development experience with Spark (PySpark), Python and SQL
    Extensive knowledge building data pipelines
    Hands on experience with Databricks Development
    Strong experience developing on Linux OS
    Experience with scheduling and orchestration (e.g. Databricks Workflows, airflow, prefect, control-m)
    Solid understanding of distributed systems, data structures, design principles
    Comfortable communicating with teams via showcases/demos
    Agile Development Methodologies (e.g. SAFe, Kanban, Scrum)
    Experience with Databricks Unity Catalogue
    Experience in developing metadata driven framework on Databricks
    Desired qualifications/non-essential skills:
    3+ years experience with GIT
    3+ years experience with CI/CD (e.g. Azure Pipelines)
    Experience with streaming technologies, such as Kafka and Spark
    Experience building applications on Docker and Kubernetes
    Cloud experience (e.g. Azure, Google
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