Location: United States,Texas,Dallas
Job Type: Full-time
We are seeking a skilled and motivated Google Cloud Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based solutions using Google Cloud Platform (GCP). You will play a critical role in enabling data-driven decision-making across the organization by building robust data infrastructure and delivering efficient solutions.
Design, implement, and manage data pipelines using GCP tools such as Cloud Dataflow, BigQuery, Pub/Sub, and Cloud Composer.
Develop and maintain ETL/ELT workflows to ingest, transform, and load structured and unstructured data.
Collaborate with data scientists, analysts, and business stakeholders to gather data requirements and deliver scalable solutions.
Optimize data processing performance and storage costs using GCP-native services.
Implement data governance, quality, and security best practices in compliance with organizational and regulatory standards.
Build data models, data marts, and reporting datasets for analytics and BI consumption.
Integrate data from various sources such as APIs, databases, cloud storage, and third-party services.
Monitor, debug, and troubleshoot production data pipelines and cloud infrastructure issues.
Stay current with the latest GCP updates and best practices in cloud data engineering.
Bachelor's degree in Computer Science, Information Systems, or a related field.
3+ years of experience in data engineering or a similar role.
Hands-on experience with GCP services: BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Functions, Cloud Composer.
Strong SQL skills and experience with data modeling and performance tuning.
Proficiency in programming languages such as Python, Java, or Scala.
Experience with CI/CD and Infrastructure as Code tools (e.g., Terraform, Cloud Deployment Manager).
Familiarity with data security and privacy standards (e.g., HIPAA, GDPR, CCPA).
GCP Professional Data Engineer Certification or equivalent.
Experience with Apache Beam, Airflow, and/or dbt.
Familiarity with data lake and lakehouse architectures.
Experience in DevOps practices and automation.
Exposure to machine learning pipelines or MLOps on GCP is a plus.
Must be authorized to work in the United States. Sponsorship may/may not be available depending on employer.
Competitive salary based on experience and location.
Health, dental, and vision insurance.
401(k) plan with company match.
Paid time off, holidays, and sick leave.
Flexible work arrangements (remote/hybrid options).
Professional development and certification reimbursement.