Application Programmer

Develop, maintain and enhance client-intelligence/watch applications that monitor customer behavior, risk signals, and service events across insurance products. Build reliable, secure, real ‑ time monitoring, alerting and reporting capabilities that enable underwriting, fraud, retention, and customer ‑ service teams to act quickly and compliantly.

Key responsibilities:

- Design, develop and maintain backend and frontend components of the client-intelligence/watch platform (APIs, data pipelines, dashboards, alert engines).

- Implement real-time and batch data ingestion from internal systems (policy, claims, billing, CRM) and external sources (fraud feeds, credit, public records, IoT/telemetry).

- Build rule-based and ML-assisted detection/score models for risk, fraud, churn, and service anomalies; integrate models with production systems.

- Develop robust alerting and notification workflows (digest, SLA escalations, ticket creation).

- Create and maintain operational dashboards, audit trails, and KPI/reports for business users and management.

- Ensure data quality, transformation, and validation; implement logging, monitoring, and performance tuning.

- Follow security, privacy and regulatory requirements (PII handling, data retention, access controls).

- Collaborate with product owners, data scientists, QA, security and operations to deliver features and support incidents.

- Produce technical documentation, runbooks, and support knowledge transfer; participate in on-call rotations.

Required qualifications:

- Bachelor’s degree in Computer Science, Software Engineering, Information Systems or equivalent experience.

- 3+ years software development experience; preferably in financial services or insurance.

- Strong skills with at least two of: Java, C#, Python, or Node.js.

- Experience with relational databases (Postgres, SQL Server) and at least one NoSQL/streaming technology (Kafka, Elasticsearch, Redis).

- Familiarity with ETL/data pipelines, RESTful APIs and microservices architecture.

- Experience building dashboards and visualization tools (Grafana, Kibana, Power BI, Tableau).

- Understanding of data privacy and security best practices (encryption, role-based access).

- Good troubleshooting, testing, and CI/CD experience (unit tests, automated deployments).

Desired / nice-to-have:

- Experience with anomaly detection, rule engines, or ML model deployment (model scoring, feature stores).

- Knowledge of insurance domain concepts: underwriting, claims, policy lifecycle, fraud patterns.

- Cloud experience (AWS, Azure, GCP) and containerization (Docker, Kubernetes).

- Exposure to event-driven architectures and low-latency streaming systems.

- Experience with regulatory/compliance frameworks applicable to insurance and consumer data.