AWS Data Engineer

We are currently seeking an AWS Data Engineer to join our team in Bangalore, Karnataka, India.

Position Overview

The AWS Data Engineer will design, build, test, and support event-driven policy-data pipelines that move landed files and events through validation, buffering, transformation, and persistence into the Aurora-based Policy Master. Working within established architecture and data-model standards, the engineer will develop secure and reusable components using S3, Lambda, SQS, EventBridge, Python or Java, SQL, and AWS monitoring services. The role is accountable for data accuracy, idempotent processing, lineage, reconciliation, performance, recoverability, and production support across the ingestion-to-serving lifecycle.

Required Skills and Experience

• 57 years of data-engineering or backend-engineering experience, including at least 3 years of hands-on development on AWS.
• Strong hands-on experience with AWS S3, Lambda, SQS, EventBridge, CloudWatch, IAM, KMS, Secrets Manager, and related serverless integration patterns.
• Proficiency in Python; Java experience is valuable. Strong SQL skills and practical experience developing against PostgreSQL or Amazon Aurora PostgreSQL.
• Experience building event-driven and batch ingestion pipelines, schema validation, transformations, canonical data mappings, error handling, retry, dead-letter queues, replay, idempotency, and audit logging.
• Strong understanding of JSON and relational data structures, data contracts, schema evolution, reference data, data quality rules, reconciliation, lineage, and metadata capture.
• Experience writing unit and integration tests and using Git-based development, code review, CI/CD pipelines, automated deployment, and environment-specific configuration.
• Working knowledge of API-driven data consumption, REST or OpenAPI contracts, authentication and authorization, and collaboration with microservices teams.
• Ability to troubleshoot distributed workloads using logs, metrics, traces, CloudWatch dashboards, and correlation identifiers; familiarity with performance and cost optimization.
• Understanding of secure cloud engineering including least-privilege IAM, encryption in transit and at rest, VPC integration, secrets management, and protection of sensitive policy data.
• Experience with insurance policy data, policy administration platforms such as OIPA, mainframe feeds, canonical insurance models, or operational data stores is preferred; AWS Developer or Data Engineer certification is an advantage.

Duties

• Develop ingestion components that receive landed policy data in S3 and perform file, event, schema, completeness, and business-rule validation.
• Build Lambda and SQS processing patterns with appropriate message visibility, batching, ordering where required, retry, dead-letter handling, replay, and duplicate-event protection.
• Implement transformations from source-specific policy feeds into the approved canonical Policy Master structure, preserving source lineage and effective-dated history where required.
• Write optimized SQL and data-access logic to load Aurora PostgreSQL while maintaining referential integrity, transaction control, auditability, and high-throughput processing.
• Develop reconciliation controls across source, landing, transformed, and target data, including record counts, control totals, reject reporting, and explainable exception handling.
• Create automated unit, component, and integration tests and participate in peer reviews to ensure code quality, security, maintainability, and adherence to engineering standards.
• Instrument pipelines with structured logging, metrics, alerts, trace or correlation IDs, and operational dashboards; ensure failures are observable and recoverable.
• Partner with the data modeler, solution architect, API engineer, DevSecOps engineer, test engineer, and policy SMEs to refine stories, mappings, contracts, and acceptance criteria.
• Support CI/CD deployment and infrastructure configuration, diagnose issues across development through production, and contribute to runbooks and support procedures.
• Tune Lambda, SQS, Aurora interactions, data access, and processing patterns for performance, resilience, concurrency, and cost while meeting defined service levels.
• Participate in production-readiness reviews, cutover, hypercare, incident resolution, root-cause analysis, and remediation of defects or technical debt.