Position Title : Data Engineer (Data Conversion - SQL and AWS)Department : Global Delivery

Profile source system data to identify data quality gaps, mapping requirements, and transformation logic.
Investigate data anomalies, trace root causes across related tables, and document findings for stakeholder review.

Design and maintain SQL staging views that transform legacy data into target system format.
Create and maintain lookup/reference views that map source system codes to target platform values.
Build stored procedures for data population and scope management across multiple product lines.

Build and execute validation queries, reconciliation checks, and load report resolution workflows.
Participate in iterative data quality cycles (profile, fix, reload, validate) until target thresholds are met.
Implement data validation checks, monitor data quality, and address issues related to data integrity and accuracy.

Collaboration with Stakeholders:
- Resolve load report findings and deliver clean data packages.
- Maintain ongoing communication with internal teams to ensure requirements are fully understood and reflected in delivered solutions.
- Provide regular updates on project progress, issues, and milestones.
Automation & Tooling:

Document data mappings, transformation logic, product specifications, and data dictionaries.
Create and maintain comprehensive technical documentation including SQL scripts, process workflows, and integration specifications.
Share knowledge and expertise with team members, promoting best practices and fostering a culture of collaborative learning.

Leverage AI coding assistants (e.g., Claude, Copilot) to accelerate SQL development, data profiling, script generation, and documentation.
Write effective prompts to guide AI tools for complex data analysis, code generation, and automated reporting.
Review, validate, and refine AI-generated outputs to ensure accuracy and alignment with business requirements.

Stay up to date with emerging data engineering technologies, AI tooling, and best practices to improve solution delivery.
Absorb insurance domain knowledge through mentorship, documentation review, and hands-on data exploration.

Write clean, efficient, and maintainable code, adhering to coding standards and best practices.

2-5 years of experience
Proficiency in SQL Server (queries, views, stored procedures, CTEs, window functions) and ETL (Extract, Transform, Load) processes
AWS: RDS, S3, Secrets Manager, or equivalent cloud database experience
Proficiency in Excel for data analysis and legacy data review
GitHub
Experience with project management software (e.g., Jira)
Expertise in Agile methodologies (e.g., Kanban, Scrum) for managing delivery
Experience ensuring secure data handling, especially sensitive financial data and personally identifiable information (PII)
Proficient in data validation and data cleansing practices
Experience using AI coding assistants (Claude, GitHub Copilot, or similar) for development workflows, prompt engineering, and automated analysis
1. Competencies and Skills:
Excellent communication and interpersonal skills, capable of conveying complex technical concepts to non-technical stakeholders.
Strong problem-solving abilities and analytical thinking.
Self-directed learner who thrives with mentorship — eager to absorb insurance domain knowledge and conversion methodology.
Ability to work on multiple projects simultaneously while ensuring high-quality delivery.
Attention to detail, with a focus on delivering accurate and validated data.
1. Highly Preferred:
Prior data migration/conversion project experience
Experience with policy administration systems
Experience in the insurance/financial industry