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.