Gcp Data Engineer

Data Engineering & Pipeline Development

• Design, develop, and maintain scalable batch and real-time data pipelines on Google Cloud Platform (GCP).
• Build reliable and efficient data ingestion, transformation, and processing frameworks using BigQuery, Dataflow, Dataproc, BigTable, Pub/Sub, Cloud Storage, and Data Fusion.
• Create and operationalize data pipelines by integrating multiple enterprise data sources while ensuring data quality, consistency, and availability.
• Develop prototype analytics pipelines to generate business insights and support product innovation.

AI/ML & Predictive Analytics

• Build, train, and optimize machine learning models for VIN-level predictive risk scoring, loss ratio forecasting, and high-utilization contract identification.
• Perform advanced Exploratory Data Analysis (EDA) on historical claims, contracts, and operational datasets from sources such as OWS, PTS, UDB, and DMS.
• Engineer business-driven features, including complex metrics such as Claim vs. Unclaimed Learnings and High Time In Service (HTIS).
• Support AI/ML solution design, experimentation, and prototype development using Python and GCP services.

MLOps & Model Lifecycle Management

• Manage the complete machine learning lifecycle, including:
• Data preprocessing and feature engineering
• Model training and validation
• Deployment and productionization
• Performance monitoring and retraining
• Implement MLOps best practices to ensure model scalability, reliability, and maintainability.
• Monitor model performance and proactively address model drift and data quality issues.

Cloud Platform & Architecture

• Design, build, secure, monitor, and optimize data processing systems on Google Cloud Platform.
• Develop expertise in Google technologies, architectures, and data structures to support future product development and business initiatives.
• Implement cloud-native solutions following scalability, security, and cost-optimization best practices.

Data Quality, Testing & Governance

• Perform unit testing, integration testing, and validation of data pipelines and analytical solutions.
• Identify and resolve defects, data inconsistencies, and performance bottlenecks.
• Establish data quality checks, monitoring frameworks, and governance standards.
• Ensure compliance with organizational policies, ethical AI practices, and data privacy regulations.

DevOps & Automation

• Utilize Git, Jenkins, Terraform, Tekton, and CI/CD pipelines to automate deployments and infrastructure management.
• Support Infrastructure as Code (IaC) practices for cloud resource provisioning and configuration management.
• Implement automation to improve deployment efficiency, reliability, and repeatability.

Collaboration & Agile Delivery

• Collaborate with business stakeholders, product owners, data scientists, and engineering teams to understand requirements and deliver data-driven solutions.
• Participate actively in Agile ceremonies, including:
• Sprint Planning
• Backlog Grooming & Prioritization
• Daily Standups
• Sprint Reviews
• Retrospectives
• Provide technical recommendations and contribute to product roadmap discussions.

Technical Skills

• Strong proficiency in Python, SQL, and Google Cloud Platform (GCP).
• Experience with Big Data technologies and distributed data processing frameworks.
• Knowledge of machine learning, predictive analytics, feature engineering, and model deployment.
• Familiarity with DevOps, CI/CD, monitoring, and cloud security best practices.