Euclid Innovations
GCP Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GCP Data Engineer in Charlotte, NC (Hybrid - 3 days/week) for a long-term contract. Requires 12+ years of experience, strong Spark and Python skills, GCP expertise, and data migration experience.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
600
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🗓️ - Date
July 25, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
Hybrid
-
📄 - Contract
Unknown
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🔒 - Security
Unknown
-
📍 - Location detailed
Charlotte, NC
-
🧠 - Skills detailed
#Batch #Data Pipeline #Data Engineering #Data Migration #GCP (Google Cloud Platform) #Migration #Spark (Apache Spark) #Cloud #AI (Artificial Intelligence) #Airflow #Python #HDFS (Hadoop Distributed File System) #ML (Machine Learning) #BigQuery #SQL (Structured Query Language) #SQL Server #Data Quality #Storage
Role description
Role: GCP Data Engneer
Location: Charlotte, NC ( Hybrid - 3Days per week )
Duration: Long Term Contract
Experience: 12+Years
Mode of Interview: F2F ( Final call )
Handle data migration and pipeline modernization to support ML training and inference on GCP.
Skills Required:
• Strong Spark + Python coding (MANDATORY)
• GCP (Vertex AI mandatory ecosystem)
• Data migration (on-prem → cloud)
• HDFS / NFS / SQL Server / object stores
• Data pipelines (batch/streaming)
• Data quality, schema, lineage
• BigQuery + Cloud Storage
• Airflow/Dataproc
• Hybrid: Some workloads on DPC (private cloud)
• Collaboration
• ML + Data + Inference teams work closely
Preferred:
• Dataplex / governance tools
• Feature engineering / feature store
• Experience supporting ML workloads
• Platform & Environment
Role: GCP Data Engneer
Location: Charlotte, NC ( Hybrid - 3Days per week )
Duration: Long Term Contract
Experience: 12+Years
Mode of Interview: F2F ( Final call )
Handle data migration and pipeline modernization to support ML training and inference on GCP.
Skills Required:
• Strong Spark + Python coding (MANDATORY)
• GCP (Vertex AI mandatory ecosystem)
• Data migration (on-prem → cloud)
• HDFS / NFS / SQL Server / object stores
• Data pipelines (batch/streaming)
• Data quality, schema, lineage
• BigQuery + Cloud Storage
• Airflow/Dataproc
• Hybrid: Some workloads on DPC (private cloud)
• Collaboration
• ML + Data + Inference teams work closely
Preferred:
• Dataplex / governance tools
• Feature engineering / feature store
• Experience supporting ML workloads
• Platform & Environment
