Job Description :

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 :

1. Data preprocessing and feature engineering

2. Model training and validation

3. Deployment and productionization

4. 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 :

1. Sprint Planning

2. Backlog Grooming & Prioritization

3. Daily Standups

4. Sprint Reviews

5. 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.