Dot NET Data Engineer

Job Description

Role Overview

The .NET Data Engineer will own the data backbone of an AI-driven submission management and Reserving platform for an insurance company . The .NET data engineer will be responsible for integrating, transforming, and consolidating data from various structured and unstructured sources into formats suitable for analytics and reporting. They design and maintain data pipelines, data warehouses, and high-performance data stores, ensuring data integrity, efficiency, and reliability according to business requirements

The candidate must be working primarily in C#/.NET, SQL Server, and Azure, with React on the front end, with preference for candidates who understand insurance and reserving concepts.

Must-Have Skills

Data Import, ETL & SQL

•      Design repeatable processes to import data from CSV files and external APIs into SQL Server tables, then transform and cleanse it into production tables.

•      Write and maintain SQL stored procedures (SPs) for data migration and transformation, using parameters and variables so the same procedure can be reused across data sources, environments, or business units (LOBs).

•      Ensure stored procedures and .NET import tools are safely re-runnable (idempotent) handling duplicates, missing data, and partial failures without corrupting data.

API & Third-Party Integrations

•      Build and maintain .NET services that integrate with third-party APIs (e.g., Salesforce) for data enrichment and synchronization.

•      Handle authentication flows (OAuth client credentials, Bearer tokens) and design request/response models that map cleanly to C# classes.

.NET Application Development

•      Design and build .NET / C# applications — Azure Functions (isolated worker model) and console utilities — that call external APIs, process data in bulk, and write results to SQL Server.

•      Use dependency injection, configuration binding (appsettings.json, User Secrets, Azure App Configuration, Key Vault), and structured logging as standard patterns across all new services.

•      Implement asynchronous, concurrent processing (async/await, SemaphoreSlim-based bounded concurrency) to handle bulk data volumes efficiently without overwhelming downstream APIs.

•      Build resilient HTTP client code: authentication (OAuth/Bearer tokens), retry logic with backoff for transient failures, and proper error handling so a single bad record does not break an entire batch run.

•      Work with Entity Framework Core and LINQ queries for reading and writing application data in SQL Server.

•      Write and maintain unit and integration tests for data-processing logic.

React – Front End

•      Strong experience in React.js, JavaScript/TypeScript, and front-end best practices, with the ability to build responsive, scalable, and high-performance user interfaces.

•      Develop and maintain web applications using React.js; build reusable UI components and front-end libraries for future use.

•      Collaborate with backend developers to deliver high-quality, end-to-end solutions.

Data Quality, DevOps & Documentation

•      Validate data accuracy after each import/migration run (row counts, key-field checks, spot-checks against source data).

•      Use Git for version control (branching, pull requests) and Azure DevOps YAML pipelines for CI/CD (build, test, multi-environment deployment).

•      Document data flows, mapping logic, and known data quality issues for the rest of the team.

Reporting & KPIs

•      Build and maintain KPI queries/views and reporting datasets to support business and operational dashboards.

•      Work with stakeholders to translate reporting requirements into reliable, well-documented SQL views or stored procedures.

Good-to-Have Skills

Insurance Domain Knowledge

•      Familiarity with commercial insurance and underwriting concepts and the submission lifecycle (intake, triage, clearance, referral, quote).

•      Understanding of common insurance data structures and documents, such as ACORD forms, loss runs, and statements of value (SOVs).

•      Exposure to broker, policy, and submission data models.