Key Responsibilities:

1. Collaborate closely with Data Science teams to receive insurance-related analytical and predictive models, understand business requirements, model logic, and expected outcomes prior to implementation.

2. Review and refactor DS-provided model code to remove hardcoded values and environment-specific logic, and parameterize configurations using Python, SQL, and shell scripting for portability and scalability.

3. Standardize model inputs, configuration parameters, and execution workflows to ensure consistent behavior across development, testing, and production environments.

4. Integrate validated Data Science models into the GBD (Global Business Data) enterprise data platform, ensuring adherence to data architecture, security, and governance standards.

5. Design, develop, and maintain Apache Airflow DAGs to orchestrate data pipelines, model execution schedules, and upstream/downstream dependencies.

6. Support model lifecycle management and experimentation using Kubeflow, enabling reproducible, version-controlled, and scalable machine learning workflows.

7. Perform end-to-end model testing and validation using enterprise datasets stored in AWS S3 and processed via AWS EMR and AWS Glue, ensuring data accuracy, performance, and reliability.

8. Conduct data quality checks, output validation, reconciliation, and performance analysis, and document results for Data Science review and approval.

9. Collaborate with Data Science teams during validation cycles, address feedback, resolve issues, and finalize model implementations prior to production release.

10. Prepare and deploy approved models into production environments, following established release management and change control processes.

11. Deploy, monitor, and support production workflows using AWS services including EC2 instances, Lambda functions, S3 buckets, and Glue jobs.

12. Track development tasks, enhancements, defects, and production issues using Jira, ensuring timely resolution and transparent communication across teams.

13. Troubleshoot pipeline failures, model execution issues, and infrastructure-related incidents, and implement corrective actions to maintain system stability.

14. Use VS Code for development and debugging, Bitbucket for version control and code reviews, Confluence for documentation, and Jira for Agile project tracking and sprint management.

15. Participate in Agile ceremonies such as sprint planning, stand-ups, and retrospectives, providing technical updates and supporting cross-functional collaboration.

Required Skills and Qualifications:

1. Minimum 3 years of relevant experience in data engineering.

2. Strong proficiency in Python, SQL, and shell scripting.

3. Experience with Apache Airflow for workflow orchestration.

4. Knowledge of Kubeflow for machine learning lifecycle management.

5. Hands-on experience with AWS services (S3, EMR, Glue, EC2, Lambda).

6. Experience with data quality validation and testing frameworks.

7. Proficiency in VS Code, Bitbucket, Confluence, and Jira.

8. Understanding of data architecture, security, and governance principles.

9. Experience with Agile methodologies and ceremonies.

10. Strong problem-solving and troubleshooting skills.

11. Excellent collaboration and communication abilities.

Preferred Qualifications:

1. Experience working with Data Science teams and machine learning models.

2. Proven track record of deploying and maintaining production data pipelines.

3. Experience in the insurance or financial services domain is a plus.

Send resumes to hr@narveetech.com