Responsibilities
- Develop and sustain robust data pipelines supporting Medex monitoring systems.
- Create and refine scripts in SQL, Python, or R to automate data processing workflows.
- Investigate the underlying causes of data discrepancies and coordinate fixes with involved teams.
- Oversee periodic monitoring updates and guarantee prompt delivery of refreshed datasets.
- Strengthen data validation rules, implement additional quality controls, and improve monitoring metrics.
- Assist in in-depth analysis of claims data, risk exposure, trends, and portfolio outcomes.
- Automate routine monitoring tasks such as data transfers, script executions, and scheduled updates.
- Refine dashboards, KPIs, and supporting data models to ensure accuracy and uniformity.
- Build standardized tools and templates to streamline monitoring procedures.
- Collaborate with data engineering, actuarial, pricing, and accounting teams to align data standards.
- Support user acceptance testing for dashboard updates, pipeline improvements, and logic adjustments.
- Clearly convey findings, data issues, and improvement suggestions in a timely manner.
About You
Bachelor’s degree in Statistics, Mathematics, Data Science, Engineering, Economics, Actuarial Science, or a related discipline is required. Candidates should have 3–5 years of hands-on experience in data engineering and analytics, ideally within insurance. Proficiency in SQL and PySpark is essential, along with practical experience in relational databases. Working knowledge of Python or R for automation and data manipulation is required. Experience with Power BI or comparable visualization platforms is expected. Familiarity with data pipeline architecture, validation methods, and monitoring systems is important. Candidates should demonstrate strong analytical thinking and experience with GLMs or machine learning models. Must be able to consistently manage recurring tasks while driving process improvements. Excellent communication skills and the ability to work across technical and non-technical groups are necessary. A proactive approach to improving data integrity and operational effectiveness is key.
Other
Individuals who are re-entering the workforce after a career break are encouraged to apply if their background aligns with the role’s requirements.
