Data Engineering built for Insurance companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Insurance companies, this means navigating NAIC regulations (US), Solvency II (EU) while shipping fast.
Who needs Data Engineering for Insurance?
- Manual claims processing slowing settlement
- Legacy policy administration systems
- Risk modeling requiring better data infrastructure
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Claims processing speed and risk modeling accuracy now directly drive customer retention in insurance.
Every Insurance company eventually needs reliable Data Engineering — we make that step less risky.
Talk to our Insurance Data Engineering specialists this week
Book a free 30-minute discovery callWhat's actually blocking Insurance businesses from shipping Data Engineering
- Manual claims processing slowing settlement
- Legacy policy administration systems
- Risk modeling requiring better data infrastructure
- Customer expectations for digital-first experiences
- Fraud detection across claims workflows
How we tailor Data Engineering to what Insurance teams actually need
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- NAIC regulations (US)
- Solvency II (EU)
- GDPR
- State insurance licensing requirements
Our Approach
We approach Insurance builds around the constraint that matters most: manual claims processing slowing settlement. Every engagement in this space gets NAIC regulations (US)-aware data handling and compliance review before launch, not after.
See our case studiesTalk to our Insurance Data Engineering specialists this week
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple insurance projects addressing challenges like manual claims processing slowing settlement. We helped an insurtech platform cut claims processing time by 40% with an automated underwriting workflow.
For insurance clients, we pay close attention to NAIC regulations (US), Solvency II (EU), building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for insurance clients include manual claims processing slowing settlement and legacy policy administration systems. We tailor our discovery process to surface the specific version of these problems your business faces.