Google Cloud Platform Data Engineer with ML

Role: Google Cloud Platform 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 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.