Job Highlights
Experience with Google Cloud Platform (GCP)
Python and Machine Learning toolsets
Database management (SQL and noSQL)
Job Description
POSITION SUMMARY:
The Data Engineer will collaborate with customer’s Data Platform/Analytics teams, providing deep technical subject matter expertise for successfully deploying data solutions using modern data/analytics technologies on Google Cloud. You will be responsible for managing ETL Pipelines, Data Architecture, and Digital Marketing related data projects.
The position needs to solve problems proactively for Customer, work closely with IT teams to improve the quality and value of Customer data, respond to Customer’s data strategy.
RESPONSIBILITIES:
Help customer to perform technical assessments of current state of enterprise data and architect a path to transformation into a modern data powered enterprise
Create technical, security, data and operational architecture and design blueprints incorporating modern data technologies and cloud data services demonstrating modernization value proposition
Design, develop, test, implement and support technical solutions across a full stack of development tools and technologies
Design, build and operationalize large-scale enterprise data solutions and applications using GCP data and analytics services including- Cloud DataProc, Cloud Dataflow/Apache Beam, Cloud Data Fusion, Composer/Airflow, Big Table, Cloud BigQuery, Cloud PubSub, Cloud storage Cloud Functions & GitHub
Design and build production data pipelines from data ingestion to consumption within a hybrid big data architecture, using Cloud Native solutions, Java, Python, Scala, or Golang
QUALIFICATIONS:
Bachelor's degree in a technical discipline
Holder of Google Cloud Data Engineer Certification or Google Professional Cloud Architect Certification, or equivalent certifications issued by other cloud providers
One year plus of experience with architecting and implementing data and analytics platforms on GCP cloud
One year plus of experience with Python and Machine Learning toolsets (Scikit-learn, Numpy, Pandas) and database management (SQL and noSQL)
Deep knowledge and understanding of technical aspects of data and database systems
Deep understanding of relevant development languages, tools, frameworks, utilities, and technical dependencies
Experience with container technologies like Docker and Kubernetes is a plus
