Data Platform & Systems Engineer

ph3Position Details /h3 ul liReporting Manager: Director, Digital Learning Systems /li liStatus: Salary, Exempt /li liType: Regular, Full-Time (12 Months) /li liProgram: Stellar Virtual OMG - Corp /li /ul h3Overview /h3 pThe Data Platform Systems Engineer will be responsible for developing, maintaining, and progressively assuming day-to-day ownership of Stellar Virtual's data platform, including its ingestion pipelines, data warehouse, transformation layer, orchestration, integrations, and delivery systems. The engineer works across the full stack—Python, Azure, SQL, dbt, orchestration, and downstream delivery to ensure the platform is reliable, scalable, maintainable, and operationally sound. Effective use of AI-assisted development and automation is an essential part of the role, both to accelerate engineering work and to improve system monitoring, management, and reliability. As familiarity with the platform grows, the engineer will be expected to work increasingly independently, identify opportunities for improvement, and contribute meaningfully to technical architecture and the platform roadmap. Success in this role requires strong engineering judgment, ownership, clear communication, and consistent follow-through, in support of Stellar Virtual's mission of empowering families and unleashing potential. /p h3Essential Duties /h3 ul liData Ingestion ul liBuild, maintain, and improve Python-based ingestion pipelines integrating APIs, SFTP sources, files, and other external systems. /li liDesign pipelines using reliable engineering patterns, including incremental processing, idempotency, retry and backoff handling, checkpointing, and recoverability. /li liDiagnose and resolve source-system, authentication, schema, and data-transfer issues. /li liDevelop reusable ingestion patterns and shared components to reduce duplication and improve maintainability across integrations /li /ul /li liData Modeling Transformation ul liDevelop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers. /li liTranslate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications. /li liEvaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales. /li liMaintain clear lineage and consistent modeling conventions across the warehouse. /li /ul /li liData Modeling Transformation ul liDevelop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers. /li liTranslate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications. /li liEvaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales. /li liMaintain clear lineage and consistent modeling conventions across the warehouse. /li /ul /li liOrchestration Pipeline Operations ul liBuild and maintain orchestration across ingestion, data movement, transformation, validation, and delivery workflows. /li liManage dependencies, scheduling, retries, failure handling, and recovery across multi-stage pipelines. /li liTroubleshoot failed or degraded production jobs and perform root-cause analysis. /li liImprove pipeline reliability, runtime, scalability, and operational simplicity over time. /li liDevelop automation that reduces manual intervention and supports reliable day-to-day operation of the platform. /li /ul /li liData Quality, Testing Validation ul liDesign and implement data-quality checks across all pipeline stages: ingestion to Blob, Blob to SQL, SQL to dbt models, and dbt to downstream outputs. /li liBuild and maintain dbt tests /li /ul /li /ul /p #J-18808-Ljbffr