Senior GCP Data Engineer With AI/LLM (W2 Position)

GCP, Big Data, Data Warehousing, Artificial Intelligence & Expert Systems, API

• GCP - Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub.
• Big Data - Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics.
• Data Warehousing - Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift). For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behavior.
• Artificial Intelligence & Expert Systems - Experience developing or integrating AI/ML models and rule-based expert systems. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions.
• API - Experience designing, building, and consuming RESTful or gRPC APIs. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.

Skills Preferred:

Google Cloud Platform

• Google Cloud Platform - Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse., Senior Engineer Exp: 10+ years Data Engineering work experience

Experience Preferred:

• Operational Excellence: Using Terraform, Git, and Airflow to ensure reproducible, secure, and cost-optimized cloud infrastructure.
• Governance & Quality: Prioritizing data lineage, PII protection, and observability to maintain high trust in data assets.

• Collaboration: Acting as a bridge between technical teams (Data Science, Security) and business stakeholders to deliver self-service analytics. Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.

• Proven experience in building and deploying RAG systems, including the use of Vector Databases.
• Proficiency in Python programming.
• Solid experience with SQL for data manipulation and querying.
• Hands-on experience with Google Cloud Platform (GCP) services relevant to AI/ML.
• Basic understanding and practical experience with Machine Learning model fine-tuning.
• Familiarity with data engineering concepts and practices.
• Expertise in prompt engineering techniques for interacting with LLMs.
• Experience with the OpenAI SDK.
• Experience developing robust APIs, preferably with FastAPI.
• Proficiency with version control systems (e.g., Git).
• Experience with containerization technologies (e.g., Docker).