About the Role:
As a Data Engineer on the Conversions team, you will play a critical role in migrating insurance policy data from administration systems to a modern policy administration platform. Your primary responsibility is to profile source data, build staging pipelines, resolve data quality issues, and deliver validated data loads. This role offers direct mentorship from an experienced conversion manager and exposure to the full data migration lifecycle from initial discovery through production cutover. This position requires technical expertise in SQL and data engineering, combined with effective communication to support iterative delivery cycles
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Key Responsibilities :
Data Profiling & Analysi s:
- 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.
Staging Pipeline Developmen t:
- 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.
Data Validation & Quality :
- 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 Stakeholder s:
- 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 & Toolin g:
Maintain and populate target staging databases in cloud environments (AWS RDS).
Documentation & Knowledge Sharin g:
- 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.
AI-Assisted Developmen t:
- 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.
Continuous Learning & Innovatio n:
- 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.
Code Quality and Best Practice s:
Write clean, efficient, and maintainable code, adhering to coding standards and best practices.
Qualification s:
- 2-5 years of experience
Highly Preferred:
- Prior data migration/conversion project experience
- Experience with policy administration systems
- Experience in the insurance/financial industry
- 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
Competencies:
- • 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.
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