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# GCP Python Data Engineer

- **Company:** Capgemini
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $115,000.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Batch Processing, BigQuery, Cloud Computing, Cloud Storage, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Warehousing, Data Flow Control, Identity and Access Management, Python (Programming Language), Query Optimization, Cloud Services, Standard Sql, Cloudera, Search Technologies, Software Engineering, Data Streaming, Unstructured Data, Enterprise Data Management, Data Logging, Data Processing, Scripting, Google Cloud, Data Storage Technologies, Cloud Monitoring, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Agentic-AI, Event Driven Architecture, Data Lakes, AI Platforms, Pyspark, Deployment Automation, Data Management, Machine Learning Operations, Google Gemini, Cloud Optimization, Terraform, Data Pipelines
- **Published:** October 26, 2026
- **Apply:** https://www.manhattanjobs.com/job.asp?id=3468572055&tx=FJ8582FFQ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 5+ years of data engineering, software engineering, or related experience.
 * 2+ years of hands-on Google Cloud Platform (GCP) experience.
 * 2+ years of professional Python development experience.
 * Experience developing batch and real-time data pipelines.
 * Advanced SQL development and query optimization skills.
 * Experience with data warehousing, ETL/ELT, and modern data architecture patterns.
 * Strong understanding of cloud-native data engineering practices., * Experience with Vertex AI and Google AI services.
 * Experience building AI/ML data platforms and pipelines.
 * Knowledge of Large Language Models (LLMs) and GenAI concepts.
 * Experience with Gemini models, Agentic AI frameworks, Prompt Engineering, RAG architectures, and Vector Search.
 * Experience with Dataproc, Spark, or PySpark.
 * Familiarity with event-driven architectures.
 * Experience with Terraform or Infrastructure as Code.
 * Understanding of cloud cost optimization and FinOps practices.
 * Financial Services industry experience.
 * Google Cloud Professional Data Engineer Certification.

## Description

We are seeking a highly skilled GCP Python Data Engineer to design, build, and optimize scalable cloud-based data solutions on Google Cloud Platform (GCP). The ideal candidate will possess strong Python development skills, hands-on experience with modern data engineering technologies, and expertise in building both batch and real-time data pipelines supporting analytics, AI/ML, and enterprise reporting initiatives.

Work Authorization: Candidates must be authorized to work in the United States without current or future sponsorship. No visa sponsorship, transfers, or C2C arrangements are available., Data Engineering & Pipeline Development

 * Design, develop, and optimize ETL/ELT pipelines for structured and unstructured data.
 * Build scalable batch and streaming data processing solutions using GCP technologies.
 * Develop event-driven data processing solutions leveraging Pub/Sub and Cloud Functions.
 * Create and maintain data ingestion frameworks for enterprise data platforms.

Data Storage & Analytics

 * Design and optimize data lake, lakehouse, and data warehouse solutions.
 * Build efficient data models supporting analytics, reporting, and AI/ML workloads.
 * Optimize performance, scalability, and cost efficiency of data pipelines and queries.

Development & Automation

 * Develop robust Python-based solutions and frameworks.
 * Automate orchestration and workflow management using Cloud Composer (Airflow).
 * Implement CI/CD pipelines and deployment automation.
 * Apply software engineering best practices for testing, monitoring, and observability.

Collaboration & Support

 * Partner with business stakeholders, analytics teams, data scientists, and engineers to deliver data solutions.
 * Troubleshoot production issues and perform root cause analysis.
 * Continuously improve reliability, scalability, security, and operational excellence., Google Cloud Platform
 * BigQuery
 * Dataflow
 * Dataproc
 * Pub/Sub
 * Cloud Functions
 * Cloud Composer (Airflow)
 * Cloud Storage
 * Cloud SQL
 * IAM
 * Cloud Monitoring
 * Cloud Logging

Programming & Data Engineering

 * Python
 * SQL
 * Bash/Shell Scripting
 * ETL/ELT
 * Data Warehousing
 * Data Lake / Lakehouse Architectures
 * Batch Processing
 * Real-Time Streaming Architectures