Rivago Infotech Inc
GCP Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GCP Data Engineer with ML knowledge, offering a long-term remote contract (preferably NY/NJ) at a competitive pay rate. Requires 3-5+ years of data engineering experience, GCP expertise, and proficiency in SQL and Python.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 6, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Google Cloud Storage #Dataflow #Monitoring #AI (Artificial Intelligence) #Batch #Apache Spark #Docker #Data Science #Kubernetes #ML (Machine Learning) #Computer Science #GCP (Google Cloud Platform) #Apache Airflow #Data Lifecycle #Airflow #Storage #Data Governance #GIT #Data Engineering #Data Security #"ETL (Extract #Transform #Load)" #BigQuery #Data Pipeline #Terraform #Data Warehouse #Deployment #SQL (Structured Query Language) #Data Modeling #Spark (Apache Spark) #Apache Beam #Infrastructure as Code (IaC) #Python #Data Management #IAM (Identity and Access Management) #Security #Automation #Programming #Cloud #Data Loss Prevention
Role description
Role: GCP Data Engineer with ML knowledge
Location: Remote (Preferably NY/NJ)
Duration: Long term Project
Key Responsibilities
· Pipeline Development & ETL: Design and deploy robust batch and streaming data pipelines using Cloud Dataflow (Apache Beam) and Cloud Pub/Sub.
· Data Modeling & Warehouse: Construct and optimize data models in BigQuery for high-performance analytics and ML model consumption.
· MLOps & Deployment: Operationalize ML models developed by data scientists, transitioning models from experimentation to production environments using Vertex AI.
· Feature Engineering: Collaborate with data scientists to implement feature engineering pipelines that automate the extraction of features from raw data for training.
· Data Security & Quality: Implement data governance, privacy, and security best practices (IAM, Data Loss Prevention) throughout the data lifecycle.
· Automation: Automate data workflows and orchestration using Cloud Composer (Apache Airflow).
· Monitoring & Optimization: Monitor pipeline performance using Cloud Monitoring and optimize for cost and speed.
Required Qualifications:
· Experience: 3-5+ years of experience in data engineering, with at least 2+ years focused on GCP.
· Programming Skills: Expert-level SQL and strong Python programming skills.
· GCP Expertise: Proven experience with Cloud function, Cloudrun, GCE, GKE, BigQuery, Dataflow, Dataproc, pub-sub, Google Cloud Storage, and Vertex AI.
· Programming Skills: Expert-level SQL and strong Python programming skills.
· ML Knowledge: Understanding of machine learning fundamentals (training, testing, evaluation, drift) and feature engineering techniques.
· Strong understanding of SQL and unstructured data management.
· Hand-on experience with Docker, Kubernetes (GKE), and CI/CD tools.
· Infrastructure as Code: Experience with Terraform to provision and manage infrastructure.
· Education: Bachelor’s degree in Computer Science, Engineering, or a related field.
Preferred Qualifications
Certification:
· Google Cloud - Professional Data Engineer Certification.
· MLOps Specialization: Experience with Kubeflow or Vertex AI Pipelines.
· Data Modeling: Strong understanding of data warehouse modeling patterns (Kimball/Inmon).
Key Technologies:
· GCP Core: Cloud function, Cloudrun, BigQuery, Dataflow, Pub/Sub, Composer, Dataproc, Vertex AI.
· Languages: Python, SQL
· Frameworks: Apache Beam, Apache Spark.
· Tools: Terraform, Git, Docker, Kubernetes.
Role: GCP Data Engineer with ML knowledge
Location: Remote (Preferably NY/NJ)
Duration: Long term Project
Key Responsibilities
· Pipeline Development & ETL: Design and deploy robust batch and streaming data pipelines using Cloud Dataflow (Apache Beam) and Cloud Pub/Sub.
· Data Modeling & Warehouse: Construct and optimize data models in BigQuery for high-performance analytics and ML model consumption.
· MLOps & Deployment: Operationalize ML models developed by data scientists, transitioning models from experimentation to production environments using Vertex AI.
· Feature Engineering: Collaborate with data scientists to implement feature engineering pipelines that automate the extraction of features from raw data for training.
· Data Security & Quality: Implement data governance, privacy, and security best practices (IAM, Data Loss Prevention) throughout the data lifecycle.
· Automation: Automate data workflows and orchestration using Cloud Composer (Apache Airflow).
· Monitoring & Optimization: Monitor pipeline performance using Cloud Monitoring and optimize for cost and speed.
Required Qualifications:
· Experience: 3-5+ years of experience in data engineering, with at least 2+ years focused on GCP.
· Programming Skills: Expert-level SQL and strong Python programming skills.
· GCP Expertise: Proven experience with Cloud function, Cloudrun, GCE, GKE, BigQuery, Dataflow, Dataproc, pub-sub, Google Cloud Storage, and Vertex AI.
· Programming Skills: Expert-level SQL and strong Python programming skills.
· ML Knowledge: Understanding of machine learning fundamentals (training, testing, evaluation, drift) and feature engineering techniques.
· Strong understanding of SQL and unstructured data management.
· Hand-on experience with Docker, Kubernetes (GKE), and CI/CD tools.
· Infrastructure as Code: Experience with Terraform to provision and manage infrastructure.
· Education: Bachelor’s degree in Computer Science, Engineering, or a related field.
Preferred Qualifications
Certification:
· Google Cloud - Professional Data Engineer Certification.
· MLOps Specialization: Experience with Kubeflow or Vertex AI Pipelines.
· Data Modeling: Strong understanding of data warehouse modeling patterns (Kimball/Inmon).
Key Technologies:
· GCP Core: Cloud function, Cloudrun, BigQuery, Dataflow, Pub/Sub, Composer, Dataproc, Vertex AI.
· Languages: Python, SQL
· Frameworks: Apache Beam, Apache Spark.
· Tools: Terraform, Git, Docker, Kubernetes.
