Google Cloud Platform Data Engineer NJ (Hybrid) Google Cloud Platform, BigQuery, AlloyDB, Python, Pub/Sub, Compute Engine, Cloud Monitoring.

Role: Google Cloud Platform Data Engineer 

 Location: NJ - {HYBRID}

Job Summary:

Drive robust data solutions as a senior data engineer and architect with deep expertise in Google Cloud Monitoring, Python, AlloyDB, Google Cloud Pub Sub and Google Cloud Platform BigQuery in a hybrid work model. Apply transportation and logistics domain knowledge to design scalable data platforms that improve operational efficiency, optimize supply chains and enable data driven decisions for a global enterprise.

Required Skills:

  Hands-on experience with Google Cloud Platform services. 

   Knowledge of BigQuery, AlloyDB, and Compute Engine. 

   Basic Python programming and optimization techniques. 

   Experience with monitoring and observability tools. 

   Understanding of performance testing concepts. Optimize Python, BQ, AlloyDB, AlloyDB, Pub/Sub Process.

Responsibilities:

•  Design and implement scalable data pipelines on Google Cloud Platform that ingest transform and store transportation and logistics data to support analytics and operational reporting across the enterprise. 

 •  Develop efficient and maintainable Python code for data processing validation and automation ensuring reliable end to end data workflows and reduced operational overhead. 

 •  Architect and optimize AlloyDB schemas indexes and query patterns tailored to transportation and logistics datasets to deliver high performance transactional and analytical workloads. 

 •  Configure and manage Google Cloud Pub Sub messaging solutions to support near real time event driven data integration between logistics systems tracking platforms and analytical applications. 

 •  Utilize Google Cloud Platform Big Query to design performant data models partitioning strategies and materialized views that enable fast and cost-effective analytics on large scale transportation data. 

 •  Implement comprehensive observability using Google Cloud Monitoring to track pipeline health resource utilization application metrics and alert thresholds ensuring high availability and rapid incident response. 

 •  Collaborate with cross functional stakeholders to translate transportation and logistics process requirements into concrete data architecture designs that directly improve route optimization asset utilization and delivery reliability. 

 •  Document data models data flow diagrams and architectural patterns with clear technical and domain context to enable future enhancement onboarding and effective cross team collaboration. 

 •  Establish and enforce data quality standards validation rules and monitoring dashboards focused on shipment tracking order management and inventory visibility to increase trust in analytics outputs. 

 •  Optimize cloud resource configuration and query performance for BigQuery AlloyDB and Pub Sub to balance cost efficiency with required throughput and latency targets in day shift operations. 

 •  Coordinate with operations and planning teams in a hybrid work model to assess data needs prioritize backlog items and deliver incremental improvements that enhance logistics decision making.

 •  Evaluate and propose enhancements to existing data architecture including migration strategies consolidation of data sources and improved monitoring to better support strategic transportation initiatives. 

 •  Mentor peers on Google Cloud Platform best practices cloud native data patterns and domain nuances in transportation and logistics thereby raising overall team capability and consistency in solution delivery.