Google Cloud Platform Data Engineer with ML knowledge

Role : Google Cloud Platform Data Engineer with ML knowledge

Location : Remote

Rate : $70/hr on C2C

Contract

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 Google Cloud Platform.

Programming Skills: Expert-level SQL and strong Python programming skills.

Google Cloud Platform 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

Google Cloud Platform 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.