Overview
On Site
Accepts corp to corp applications
Contract - Contract
Skills
SQL
Python
GCP
Data Engineer
Job Details
Hi,
Our client is looking Google Cloud Platform Data Engineer for Long Term Contract project in Dallas, TX (Onsite). below is the detailed requirements.
Job Title : Google Cloud Platform Data Engineer
Location : Dallas, TX (Onsite)
Duration : Long-Term Contract
Required Skills : Python, PySpark, SQL, Cloud Composer, Cloud Dataflow, Cloud Dataproc, Google Big Query
Responsibilities:
- Google Cloud Platform Data Engineer will create, deliver, and support custom data products, as well as enhance expand team capabilities.
- They will work on analyzing and manipulating large datasets supporting the enterprise by activating data assets to support Enabling Platforms and analytics.
- Google Cloud Data Engineers will be responsible for designing the transformation and modernization on Google Cloud Platform using Google Cloud Platform Services
- Responsibilities Build data systems and pipelines on Google Cloud Platform Cloud using Data proc, Data Flow, Data Fusion, Big query and Pub/Sub Implement schedules/workflows and tasks for Cloud Composer/Apache Airflow.
- Create and manage data storage solutions using Google Cloud Platform services such as BigQuery, Cloud Storage, and Cloud SQL Monitor and troubleshoot data pipelines and storage solutions using Google Cloud Platforms Stackdriver and Cloud Monitoring
- Develop efficient ETL/ELT pipelines and orchestration using Data Prep, Google Cloud Composer
- Develop and Maintain Data Ingestion and transformation process using Apache PySpark, Dataflow Automate
- Data processing tasks using scripting languages such as Python or Bash Ensuring data security and compliance with industry standards by configuring IAM roles, service accounts, and access policies.
- Automating cloud deployments and infrastructure management using Infrastructure as Code (IaC) tools such as Terraform or Google Cloud Deployment Manager.
- Participate in Code reviews, contribute to development best practices and usage of Developer Assist tools to create a robust fail-safe data pipelines
- Collaborate with Product Owners, Scrum Masters and Data Analyst to deliver the User Stories and Tasks and ensure deployment of pipelines
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