Job Title: GCP Data Engineer
Location: Houston, TX (onsite only local)
NO GC & GCEAD
Job Summary: We are seeking an experienced GCP Data Engineer to design, build, and maintain data pipelines and architectures on Google Cloud Platform. The role requires strong technical expertise in GCP services, data pipeline development, and hands-on experience with both batch and real-time processing. The ideal candidate will be adept at collaborating with stakeholders, translating business requirements into technical solutions, and ensuring the scalability and reliability of data systems.
Required Skills & Experience:
4+ years of professional experience as a Data Engineer.
Strong expertise in Google Cloud Platform (GCP) ecosystem.
Proficiency in Python for data engineering tasks.
Experience with batch and real-time data processing.
Hands-on experience with SAP SLT replication.
Strong understanding of DevOps and CI/CD practices (GitHub, Terraform).
GCP Professional Data Engineer Certification required.
Key Responsibilities:
Design, develop, test, and maintain data acquisition pipelines for large-scale structured and unstructured data.
Build and manage complex datasets to support business needs.
Develop scalable big data pipeline architectures in GCP.
Collaborate with stakeholders to identify opportunities for new data acquisition and integration.
Translate business needs into technical requirements and solutions.
Work with GCP ecosystem tools including: Python, DataFlow, DataStream, CDC, Cloud Functions, Cloud Run, Pub/Sub, BigQuery, Cloud Storage.
Implement monitoring solutions using logs and alerts for pipeline performance.
Use SAP SLT to replicate SAP tables into GCP.
Develop JSON-based messaging structures for application integration.
Apply DevOps and CI/CD practices (GitHub, Terraform) for automation and scalability.
Optimize datasets with partitioning, clustering, IAM roles, and Policy Tags for security and performance.
Manage data access with roles, authorized views, and security policies.
Build data ingestion pipelines leveraging REST APIs.
Continuously recommend improvements in data quality, governance, and efficiency.