Senior/Principle GCP Data Engineer with strong SQL and Python

Steer Clear -

No one < 3 years of experience on GCP

No one with only experience in AWS and Azure

Data Engineering Requirement

Programming

-> SQL

-> Python

-> Java (Optional)

GCP

-> BigQuery

-> Dataflow (Apache Beam)

-> Cloud Composer (Airflow)

-> GCS

-> GKE

-> Dataform (Optional to dbt)

Tools

-> dbt / Dataform(On GCP)

-> 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 GCP)

-> Security

(GCP Services IAM, KMS, DLP. Techniques ACL's, Row/Column level in BigQuery)

Deployment

-> CI/CD

-> Github

-> Cloud Build (Service in GCP) + 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 GCP 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 GCP 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.