Data Engineering & Pipeline Development

- Design, develop, and maintain scalable batch and real-time data pipelines on Google Cloud Platform (GCP).
- Build reliable and effective 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.

📌 Gcp Data Engineer (Chennai)
🏢 ALTIMETRIK
📍 Chennai