GCP Python Data Engineer

Capgemini•Atlanta, GA

2d•$80,420 - $106,050•Onsite

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Requirements

GCP Services: BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Functions (Gen2), Cloud Composer (Airflow), Cloud Storage, Cloud SQL
Programming: Python (advanced)
Query Language: SQL (advanced)
Data Processing: Batch & Streaming architectures
Scripting: Bash/Shell scripting
Concepts: Data Warehousing, ETL/ELT, Data Lake / Lakehouse architectures
5+ years of overall data engineering or software engineering experience
3+ years of hands-on Google Cloud Platform experience
3+ years of Python development
3+ years of experience building data pipelines (batch and streaming)

Nice To Haves

Experience with Dataproc (Spark/PySpark) for large-scale processing
Familiarity with event-driven architectures
Knowledge of Terraform or Infrastructure as Code
Understanding of cost optimization (FinOps)

Responsibilities

Design, build, and maintain scalable batch and real-time data pipelines using GCP services such as Dataflow, Dataproc, and Pub/Sub
Develop and optimize ETL/ELT workflows for structured and unstructured data processing
Implement event-driven data processing using Cloud Functions and Pub/Sub
Build and manage data ingestion frameworks for streaming and batch data sources.
Design and optimize data lakes and data warehouses using BigQuery and Cloud Storage
Develop efficient data models to support analytics, reporting, and machine learning workloads
Optimize performance and cost of data pipelines and queries
Develop reusable and scalable solutions using Python
Automate workflows and orchestration using Cloud Composer (Airflow)
Implement CI/CD pipelines and deployment automation
Collaborate with analytics, AI/ML, and business teams for data consumption needs
Troubleshoot data issues and perform root cause analysis
Continuously improve pipeline reliability, scalability, and performance

Benefits

Flexible work
Healthcare including dental, vision, mental health, and well-being programs
Financial well-being programs such as 401(k) and Employee Share Ownership Plan
Paid time off and paid holidays
Paid parental leave
Family building benefits like adoption assistance, surrogacy, and cryopreservation
Social well-being benefits like subsidized back-up child/elder care and tutoring
Mentoring, coaching and learning programs
Employee Resource Groups
Medical, dental, and vision coverage
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs