Backend Data Engineer– PHP, APIs & ETL

Job Description:
Client is seeking a Backend Data Engineer to build and support the backend services, data pipelines, and database structures that power the data portal. The role owns RESTful API development, ETL pipelines, synchronization and export processes, and the multi-store database foundation for both public-facing and administrative capabilities.
The ideal candidate brings strong experience in PHP 8.4 / CodeIgniter 4 service-layer design, My SQL on AWs RDS and MySQL Schema design, Redis caching, and ETL / data transformation. You will work closely with the Solution Architect, Frontend Developer, DevSecOps Engineer, and QA to deliver a secure, well-structured, scalable, and extensible data foundation.

Key Responsibilities
Backend API & Service Development
•     Design and develop RESTful API services covering data retrieval, metadata management, content management, audit logging, export generation, and data synchronization.
•     Implement cross-cutting concerns including authentication filters, rate limiting, request logging, and standardized JSON response formatting.
•     Build reusable service components and CodeIgniter 4 framework patterns that support maintainable, scalable behavior.
•     Integrate backend services with Redis caching, MySql on AWS RDS.
Data Engineering & ETL
•     Implement batch processing for data standardization, deduplication, cleansing, and aggregate generation across indicators, measures, and geographies (State, County, CSA).
•     Support synchronization between source, processed, and application-facing data structures through the one-way Dev $B"*(J Test $B"*(J Prod flow.
•     Deliver documented ETL tools and processing logic that can operate independently after handoff.
Database & Data Model Design
•     Design extensible MySQL schemas supporting onboarding of future datasets (Birth, Hospital Discharge) without application changes.
•     Build and optimize relational structures for performance, scalability, and maintainability under public query loads.
•     Implement multi-store analytical patterns with pre-aggregated data for responsive interactive queries.
•     Produce data dictionaries and technical documentation to support long-term operations.
Data Quality, Reliability & Operational Support
•     Implement data quality validation checks: schema validation, completeness, suppression preservation, referential integrity, integrity checks, and deduplication.
•     Build automated rollback and recovery mechanisms for synchronization failures with structured error logging.
•     Ensure reliable, secure, and auditable backend and data operations across all environments.

Thank you