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# GCP Data Engineer

- **Company:** Capgemini
- **Location:** Atlanta, GA, United States
- **Experience:** Experienced
- **Salary:** $112,000.0 - $148,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Business Analytics Applications, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing Security, Cloud Engineering, Cloud Storage, Continuous Integration, Data Architecture, Data Governance, Extract Transform Load (ETL), Data Systems, Data Vault Modeling, Data Warehousing, Dimensional Modeling, Distributed Computing Environment, Distributed Systems, Data Flow Control, Github, Identity and Access Management, Python (Programming Language), Cloudera, SQL Databases, Management of Software Versions, Google Cloud, Apache Spark, Gitlab, Data Lakes, Pyspark, Spark Streaming, Software Coding, Data Pipelines, Databricks
- **Published:** June 25, 2026
- **Apply:** https://www.juju.com/job/00000000gawzu2

## About the Role

+ 3+ years of experience as a Data Engineer or similar role.

 + Strong hands-on experience with Databricks, including:

 + PySpark/Spark

 + Delta Lake

 + Databricks workflows/jobs

 + Proficiency with GCP:

 + BigQuery

 + Cloud Storage

 + Dataflow or Dataproc

 + Strong coding skills in Python and SQL.

 + Solid understanding of distributed systems, data warehousing, and data architecture principles.

 + Experience with CI/CD tools (GitHub, GitLab, Azure DevOps, or similar).

## Description

We are seeking a skilled Data Engineer with hands-on experience in Databricks and Google Cloud Platform (GCP) to design, build, and optimize data pipelines and analytics solutions. The ideal candidate will have a strong background in distributed data processing, cloud architecture, and data modeling. This role partners closely with data analysts, data scientists, and business stakeholders to deliver scalable, reliable, and high-quality data products.

Your role:

 + Design, build, and maintain ETL/ELT pipelines using Databricks (PySpark, Delta Lake).

 + Optimize pipelines for performance, cost efficiency, and scalability within GCP.

 + Develop batch and streaming data processes using Spark Streaming, and related technologies.

 + Implement data solutions leveraging GCP services such as BigQuery, Cloud Storage, Dataflow, Cloud Composer, and Vertex AI integrations.

 + Apply best practices for cloud security, IAM configuration, monitoring, and cost management.

 + Build and maintain data models, including dimensional modeling and data vault structures.

 + Implement data quality frameworks, validation rules, and automated testing.

 + Manage data versioning, governance, and lineage using tools such as Unity Catalog or GCP Data Catalog.

 + Partner with cross-functional teams to gather requirements and translate them into technical designs.

 + Provide technical guidance and influence engineering best practices across the team.


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