Euclid Innovations
GCP Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GCP Data Engineer in Charlotte, NC, for 12 months at a competitive pay rate. Key skills include strong Spark and Python, data migration experience, and GCP exposure, particularly with BigQuery and Cloud Storage.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
600
-
🗓️ - Date
July 11, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
On-site
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Charlotte, NC
-
🧠 - Skills detailed
#AI (Artificial Intelligence) #Data Quality #Migration #Spark (Apache Spark) #Cloud #Batch #Python #SQL Server #GCP (Google Cloud Platform) #ML (Machine Learning) #HDFS (Hadoop Distributed File System) #BigQuery #Data Engineering #Storage #Data Pipeline #Data Migration #SQL (Structured Query Language)
Role description
GCP Data Engineer
Charlotte, NC
12 Months
Role Summary
Handle data migration and pipeline modernization to support ML training and inference on GCP.
MUST-HAVE
• Strong Spark + Python (MANDATORY)
• Experience with:
• Data migration (on-prem → cloud)
• HDFS / NFS / SQL Server / object stores
• Hands-on with:
• Data pipelines (batch/streaming)
• Data quality, schema, lineage
• GCP exposure:
• BigQuery + Cloud Storage
GOOD TO HAVE
• Dataplex / governance tools
• Feature engineering / feature store
• Experience supporting ML workloads
Platform & Environment
• Primary: GCP (Vertex AI mandatory ecosystem)
• Hybrid: Some workloads on DPC (private cloud)
• Collaboration:
• ML + Data + Inference teams work closely
GCP Data Engineer
Charlotte, NC
12 Months
Role Summary
Handle data migration and pipeline modernization to support ML training and inference on GCP.
MUST-HAVE
• Strong Spark + Python (MANDATORY)
• Experience with:
• Data migration (on-prem → cloud)
• HDFS / NFS / SQL Server / object stores
• Hands-on with:
• Data pipelines (batch/streaming)
• Data quality, schema, lineage
• GCP exposure:
• BigQuery + Cloud Storage
GOOD TO HAVE
• Dataplex / governance tools
• Feature engineering / feature store
• Experience supporting ML workloads
Platform & Environment
• Primary: GCP (Vertex AI mandatory ecosystem)
• Hybrid: Some workloads on DPC (private cloud)
• Collaboration:
• ML + Data + Inference teams work closely
