Client Details
A Fortune 500 US-based insurance giant with a global footprint spanning North America, & Europe. The company provides comprehensive insurance, reinsurance, and risk management services to individuals and enterprises. With a rapidly expanding customer base and growing regulatory pressures, the client was in the midst of a large-scale data modernization initiative.
Engagement Duration
12 months | 10 Data Engineers
The Challenge
The client had embarked on a major initiative to overhaul their legacy data architecture to support real-time decision-making and advanced analytics. However, their internal team faced a significant talent gap in the areas of modern data engineering and cloud-based platforms. Finding and onboarding the right talent locally proved time-consuming and cost prohibitive. Moreover, delays in assembling a team were threatening project milestones, with the risk of affecting compliance reporting and executive dashboards.
Our Approach
We began with a comprehensive consultation to understand the client’s specific data engineering needs, project deadlines, and the skillsets required. Within four weeks, we deployed a dedicated team of 10 highly skilled data engineers. The team brought deep experience in:
- Azure Data Factory (ADF) for data pipeline orchestration
- Databricks for scalable data processing
- Python for scripting and data transformation
- Azure Data Lake for centralized storage
The engineers were embedded into the client’s agile teams and aligned to their daily sprints, working closely with product owners, architects, and data scientists.
Key Contributions
- Designed and implemented 40+ high-performance data pipelines using ADF and Databricks.
- Migrated massive volumes of structured and unstructured data from on-prem systems to Azure cloud storage.
- Developed reusable Python scripts to automate data validation and cleansing tasks.
- Collaborated with compliance teams to ensure GDPR and HIPAA standards were met in all data flows.
The Impact
- Reduced pipeline execution times by 80%, enabling near-real-time data availability.
- Helped the company meet critical quarterly compliance reporting deadlines.
- Enabled faster decision-making for underwriting and claims teams through improved data accessibility.
