Modern Data Pipelines

Transforming Insurance Analytics with dbt and Snowflake

As part of a multi-year modernization initiative, a national multi-line insurer partnered with PremiumIQ to design and implement a modern data pipeline architecture powered by dbt and Snowflake. The goal was to create a scalable, automated, and resilient data development framework that would accelerate analytics delivery, strengthen governance, and enable enterprise-wide adoption of cloud-based data practices.

Challenge

The insurer was advancing its cloud-first strategy by migrating from a legacy IBM DataStage environment to Snowflake as its enterprise data platform. To fully realize the benefits of this migration, the organization needed a more modular and automated approach to data development that could support growth, reduce operational overhead, and provide a foundation for long-term innovation. While the existing pipelines had reliably supported core reporting needs, the insurer recognized opportunities to strengthen standardization, reusability, and automation across its ecosystem. The modernization effort aimed to create a unified framework that would enhance efficiency, data quality, and collaboration across teams.

The project focused on:

• Designing modern pipelines aligned with data contracts and modeling best practices

• Establishing a framework for orchestration, testing, error recovery, and incremental processing

• Embedding governance, observability, and deployment standards to ensure reliability and performance

• Supporting change management and adoption as data teams transitioned to new tools and workflows

Solution

The insurer partnered with PremiumIQ to develop and implement a modern data pipeline framework

with dbt as the core transformation layer and Snowflake as the enterprise data platform. The engagement began with a pilot project to establish foundational patterns, reusable code structures,

and operational best practices before scaling across business domains.

The implementation addressed three major components of the pipeline ecosystem:

1. Data at Rest

• Standardized data contracts, modeling patterns, and package organization

• Integrated external and open-format data sources

• Established reusable project structures for scalability and maintainability

2. Data in Motion

• Introduced orchestration and scheduling strategies for automation

• Implemented incremental and idempotent data models for consistency and reliability

• Added automated testing, error recovery, and materialization methods to improve performance

3. Operations and Governance

• Defined enterprise standards for deployment, documentation, and cost optimization

• Embedded observability and monitoring practices into daily operations

• Established documentation-as-code and enterprise-grade security controls

Following the successful pilot, PremiumIQ delivered architectural blueprints, technical demos, and

workflow guidance to ensure the new approach was embedded within the organization’s data culture.

Outcomes

The initiative delivered a future-ready data pipeline foundation that is now driving measurable impact

at scale:

• Accelerated migration from IBM DataStage to Snowflake-backed dbt pipelines

• Reusable modular framework promoting consistent engineering practices across teams

• Enterprise-ready operations model with built-in testing, monitoring, and governance

• High adoption and engagement through enablement sessions, documentation, and workshops

• Scalable foundation for expanding use cases and retiring legacy systems

"Our goal was to help the client modernize not just their technology, but their way of working with data,” said Shanth Gopalswami, Consultant at PremiumIQ. “By pairing dbt’s development rigor with Snowflake’s performance and scalability, we built a framework that enables faster delivery, stronger governance, and lasting business value.”

By adopting dbt and Snowflake at the enterprise level, the insurer now operates with a modern,

automated data development lifecycle. The new approach empowers teams to move faster, deliver

higher-quality insights, and create a resilient foundation for advanced analytics and AI-driven

innovation.

Looking Forward

Building on this foundation, the insurer is now expanding dbt adoption across additional domains and

integrating advanced observability and AI capabilities to further enhance data reliability and time-to-

insight.