CloudIngest
W2 Opportunity // GCP Data Engineer // Atlanta, GA (Atlanta)
CloudIngest, Atlanta, Georgia, United States, 30383
Job Description: GCP Data Engineer
Location: Atlanta, GA (Hybrid)
Rate: $50/hr. on W2 (No C2C)
Summary We are seeking a highly skilled
GCP Data Engineer
to design, build, and optimize cloud-native data pipelines and analytics solutions on Google Cloud Platform. The ideal candidate has strong experience with
Python ,
BigQuery ,
Cloud Data Fusion , and core GCP services such as
Cloud Composer ,
Cloud Storage ,
Cloud Functions , and
Pub/Sub . This role requires a strong foundation in
data warehousing concepts
and scalable data engineering practices.
Responsibilities Design, develop, and maintain robust ETL/ELT pipelines on
Google Cloud Platform . Build and optimize data workflows using
Cloud Data Fusion ,
BigQuery , and
Cloud Composer . Write efficient and maintainable
Python
code to support data ingestion, transformation, and automation. Develop optimized
BigQuery SQL
for analytics, reporting, and large-scale data modeling. Utilize GCP services such as
Cloud Storage ,
Pub/Sub , and
Cloud Functions
to build event-driven and scalable data solutions. Ensure data quality, governance, and reliability across all pipelines. Collaborate with cross-functional teams to deliver clean, trusted, production-ready datasets. Monitor, troubleshoot, and resolve performance issues in cloud data pipelines and workflows.
Must-Have Skills Strong experience with
GCP BigQuery
(data modeling, SQL development, performance tuning). Proficiency in
Python
for data engineering and pipeline automation. Hands-on experience with
Cloud Data Fusion
for ETL/ELT development. Working experience with key GCP services: Cloud Composer Cloud Storage Cloud Functions Pub/Sub Strong understanding of
data warehousing concepts , star/snowflake schemas, and best practices. Solid understanding of cloud data architecture and distributed processing.
Good-to-Have Skills Experience with
Vertex AI
for ML pipeline integration or model deployment. Familiarity with
Dataproc
(Spark/Hadoop) for large-scale processing. Knowledge of CI/CD workflows, Git, and DevOps best practices. Experience with Cloud Logging/Monitoring tools.
Summary We are seeking a highly skilled
GCP Data Engineer
to design, build, and optimize cloud-native data pipelines and analytics solutions on Google Cloud Platform. The ideal candidate has strong experience with
Python ,
BigQuery ,
Cloud Data Fusion , and core GCP services such as
Cloud Composer ,
Cloud Storage ,
Cloud Functions , and
Pub/Sub . This role requires a strong foundation in
data warehousing concepts
and scalable data engineering practices.
Responsibilities Design, develop, and maintain robust ETL/ELT pipelines on
Google Cloud Platform . Build and optimize data workflows using
Cloud Data Fusion ,
BigQuery , and
Cloud Composer . Write efficient and maintainable
Python
code to support data ingestion, transformation, and automation. Develop optimized
BigQuery SQL
for analytics, reporting, and large-scale data modeling. Utilize GCP services such as
Cloud Storage ,
Pub/Sub , and
Cloud Functions
to build event-driven and scalable data solutions. Ensure data quality, governance, and reliability across all pipelines. Collaborate with cross-functional teams to deliver clean, trusted, production-ready datasets. Monitor, troubleshoot, and resolve performance issues in cloud data pipelines and workflows.
Must-Have Skills Strong experience with
GCP BigQuery
(data modeling, SQL development, performance tuning). Proficiency in
Python
for data engineering and pipeline automation. Hands-on experience with
Cloud Data Fusion
for ETL/ELT development. Working experience with key GCP services: Cloud Composer Cloud Storage Cloud Functions Pub/Sub Strong understanding of
data warehousing concepts , star/snowflake schemas, and best practices. Solid understanding of cloud data architecture and distributed processing.
Good-to-Have Skills Experience with
Vertex AI
for ML pipeline integration or model deployment. Familiarity with
Dataproc
(Spark/Hadoop) for large-scale processing. Knowledge of CI/CD workflows, Git, and DevOps best practices. Experience with Cloud Logging/Monitoring tools.