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

Keylent Inc; New Jersey; Hybrid; USD 150,000 - 190,000; Full time

Job summary

Keylent Inc. in New Jersey seeks a senior Google Cloud Platform Data Engineer to design robust data platforms for transportation and logistics. You will architect scalable pipelines using BigQuery, AlloyDB, Pub/Sub and Compute Engine, while maximizing performance and cost efficiency in a hybrid work setting.

The role emphasizes observability, data quality, and collaboration with cross-functional teams to drive delivery reliability and actionable insights for a global enterprise.

Qualifications

• 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 and Pub/Sub.

Responsibilities

• Design and implement scalable data pipelines on Google Cloud Platform.
• Develop Python data processing code for validation and automation.
• Architect and optimize AlloyDB schemas, indexes and queries.
• Configure and manage Google Cloud Pub/Sub for near real‑time data integration.
• Use BigQuery to design data models, partitioning and materialized views.
• Implement observability using Google Cloud Monitoring to track health and alerts.
• Collaborate with stakeholders to translate logistics needs into data architectures.
• Document data models and data flows with clear context for onboarding.
• Establish data quality standards and monitoring dashboards.
• Optimize cloud resources for cost and performance targets.

Skills

Google Cloud Platform

BigQuery

AlloyDB

Compute Engine

Python

Monitoring tools

Performance testing

Tools

Google Cloud Pub/Sub

Google Cloud Monitoring

BigQuery

AlloyDB

Compute Engine

Job description

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.