Platform Data Engineer: GCP & Microservices

Fasttek

Dearborn (MI)

On-site

USD 120,000 - 160,000

Full time

2 days ago

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Benefits offered by this job

Medical & Dental

Vision

PTO

Disability Insurance

Life Insurance

401k with match

Job summary

FastTek Global in Dearborn, Michigan seeks a Platform Software Engineer to design and build scalable data pipelines and microservices on GCP, using Java or Python and modern CI/CD practices, ensuring production readiness.

You will collaborate with data architects and software engineers to implement SOA, full-stack integration, data ingestion, governance, security, and performance tuning across the enterprise data platform, applying AI/ML where applicable.

Qualifications

Proficient in Java or Python with cloud-based data pipelines and microservices.
Experience designing and deploying SOA/microservices in GCP.
Understanding of SDLC, CI/CD, GitHub, and production release processes.

Responsibilities

Design and Build Data Pipelines: Architect, develop, and maintain scalable data pipelines and microservices supporting real-time and batch processing on GCP.
Service-Oriented Architecture and Microservices: Design and implement SOA and microservices architectures to deliver modular, flexible, scalable, and maintainable data solutions.
Full-Stack Integration: Contribute to the integration of front-end and back-end components, supporting robust data access and UI-driven data exploration.
Data Ingestion and Integration: Lead the ingestion and integration of data from multiple sources into the enterprise data platform, ensuring data is standardized and optimized for analytics.
GCP Data Solutions: Utilize GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Functions to build and manage scalable data platform solutions.
Data Governance and Security: Implement data governance, access controls, and security best practices, including GCP row-level and column-level security.
Performance Optimization: Monitor and improve the performance, scalability, reliability, and efficiency of data pipelines and storage solutions.
Collaboration and Best Practices: Partner with data architects, software engineers, and cross-functional teams to establish best practices, design patterns, and frameworks for cloud data engineering.
Automation and Reliability: Automate data platform processes to improve reliability, reduce manual intervention, and increase operational efficiency.
AI/ML: Leverage AI/ML capabilities and tools to accelerate software delivery and solve business problems at the platform level.