Senior/Principle Google Cloud Platform Data Engineer with strong SQL and Python

Role: Senior/Principle Google Cloud Platform Data Engineer with strong SQL and Python
Location- Remote
Duration- 6 Months C2H
Openings - 6 Positions

Steer Clear
No one < 3 years of experience on Google Cloud Platform
No one with only experience in AWS and Azure

Data Engineering Requirement

Programming
SQL
Python
Java (Optional)

Google Cloud Platform
BigQuery
Dataflow (Apache Beam)
Cloud Composer (Airflow)
GCS
GKE
Dataform (Optional to dbt)

Tools
dbt / Dataform(On Google Cloud Platform)
Test Automation on Data

Misc.(Good to Have):
Docker
Kubernetes
Microservices

Experience:

Data Platform Building (Mandatory)

Ingestion/Migration
Transformation/ETL
Analysis (Optional)
Visualization
(SAP BOBJ/Looker/PowerBI)
Governance
(Unitiy Catalog(tool in databricks)/Colibra(tool)/Data Catalog(Service in Google Cloud Platform)
Security
(Google Cloud Platform Services IAM, KMS, DLP. Techniques ACL's, Row/Column level in BigQuery)

Deployment

CI/CD
Github
Cloud Build (Service in Google Cloud Platform) + Terraform

Requirements:

• 10-15+ (for senior) of proven experience in modern cloud data engineering, broader data landscape experience and exposure and solid software engineering experience.
• Prior experience architecting and building successful enterprise scale data platforms in a green field environment is a must.
• Proficiency in building end-to-end data platforms and data services in Google Cloud Platform is a must.
• Proficiency in tools and technologies: BigQuery, Cloud Functions, Cloud Run, Dataform, Dataflow, Dataproc, SQL, Python, Airflow, PubSub. ----- SQL/Python Must
• Experience with Microservices architectures - Kubernetes, Docker and Cloud Run
• Experience building Symantec layers.
• Proficiency in architecting and designing and development experience with batch and real time streaming infrastructure and workloads.
• Solid experience with architecting and implementing metadata management including data catalogues, data lineage, data quality and data observability for big data workflows.
• Hands-on experience with Google Cloud Platform ecosystem and data lakehouse architectures.
• Strong understanding of data modeling, data architecture, and data governance principles.
• Excellent experience with DataOps principles and test automation.
• Excellent experience with observability tooling: Grafana, Datadog.