Purpose
Responsible for designing, building, and operating the enterprise data platform that powers analytics and eventually AI across the organization. This role is responsible for ensuring our company’s data is consolidated, trusted, and AI-ready, turning disparate source systems into a unified source of truth.
This is a hands-on builder role. The Sr. Principal Data Engineer leads by architecting and building pipelines, modernizing our legacy MS SQL Server, SSIS, and SSAS environment, and driving pragmatic migrations toward MS Fabric and modern data stack technologies, while maintaining continuity for the business every step of the way.
We are looking for someone with Property and Casualty Insurance experience who already understands how data actually moves through the business and approaches hard problems or unfamiliar situations with curiosity rather than resistance. This role is a fit for someone who:
- Owns projects and outcomes from problem to production, not just handed tasks, and can mentor others to do the same.
- Measures success by business value and the quality of outcomes, not volume of work shipped.
- Has led small teams to solve hard, ambiguous problems where the right path isn’t obvious.
- Has the ability to pivot quickly and learn fast.
Key Responsibilities
Data Platform and Warehousing
- Design, build, and maintain reliable, scalable data pipelines (ingestion, transformation, and delivery) across all organizational data sources.
- Own the architecture and operation of the enterprise data warehouse, currently MS SQL Server on-premises, and lead the technical migration toward Azure Synapse, Data Factory, and cloud-native tooling.
- Modernize and replace legacy SSIS packages with scalable, maintainable ELT/ETL frameworks; evaluate and introduce tools across the modern data stack with a pragmatic adoption approach.
- Define and own SLAs for platform performance, availability, and cost-efficiency as data volumes and use cases grow.
Data Consolidation and Integration
- Consolidate data from disparate source systems including policy administration, billing, claims, and financial platforms into a unified, trusted source of truth.
- Lead integration efforts across legacy and new systems, resolving inconsistencies in structure, definitions, and data quality at the source.
- Establish and maintain consistent data definitions, canonical data models, and integration standards across the organization.
Data Governance and Quality
- Implement data quality frameworks, lineage tracking, and metadata management appropriate to a regulated insurance environment.
- Build auditable, defensible data lineage to support internal controls and regulatory requirements.
- Contribute to MDM practices, ensuring policy, claims, billing, and producer data are clean, consistent, and trustworthy across systems.
- Apply data security and privacy controls appropriately across all pipelines and storage layers.
Analytics Enablement
- Deliver governed, well-documented data products that power BI dashboards, self-service analytics, and actuarial/financial modeling.
- Partner with underwriting, claims, actuarial, finance, and producer management teams as a data and platform provider, ensuring they have the trusted data and tooling needed to perform their own analysis.
- Evolve and extend existing SSAS analytical models, progressing toward modern semantic layer tooling aligned with the Azure migration roadmap.
Leadership and Partnership
- Serve as the most senior individual contributor on the data engineering team, setting technical standards, reviewing designs, and guiding engineers through complex problems by leading through example.
- Mentor junior and mid-level data engineers, raising the technical bar through code reviews, pairing, and documentation.
- Collaborate with technology leadership, architects, and business stakeholders to align the data platform roadmap with enterprise priorities.
- Champion engineering best practices: version control, CI/CD for pipelines, automated testing, and infrastructure-as-code.
Qualifications
- Bachelor’s degree in Computer Science or related field; advanced degree preferred.
- 10+ years of hands-on data engineering experience, including progressive responsibility on complex, enterprise-scale platforms.
- Deep, production-level expertise with MS SQL Server, query optimization, schema design, indexing strategies, and performance tuning.
- Significant hands-on experience building and maintaining SSIS pipelines; ability to assess, refactor, or replace legacy packages.
- Experience with SSAS (tabular or multidimensional) and the ability to evolve analytical models toward modern semantic layer tools.
- Proven track record migrating or extending on-prem environments toward Azure.
- Hands-on experience building data governance, lineage, and data quality frameworks, not just defining policies, but implementing them.
- Property and Casualty insurance domain experience is required. Familiarity with policy, claims, billing, commission, and producer data structures is strongly preferred.
- Experience delivering data products that serve actuarial, finance, underwriting, and claims functions as a platform provider, not the primary modeling owner.
Required Skills
- Familiarity with the following technologies.
- MS SQL Server, SSIS, SSDT, SSAS, T-SQL, Query Tuning
- Azure Synapse, Data Factory, SQL/Storage
- Data Modeling ETL/ELT Design
- Data Governance, Lineage, MDM, Quality
- CI/CD for pipelines
