Data Platform & Systems Engineer
Pubblicato il 28-09-2026 - Stellar Virtual in None
Position Details
- Reporting Manager: Director, Digital Learning Systems
- Status: Salary, Exempt
- Type: Regular, Full-Time (12 Months)
- Program: Stellar Virtual OMG - Corp
Overview
The 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.
Essential Duties
- Data Ingestion
- Build, maintain, and improve Python-based ingestion pipelines integrating APIs, SFTP sources, files, and other external systems.
- Design pipelines using reliable engineering patterns, including incremental processing, idempotency, retry and backoff handling, checkpointing, and recoverability.
- Diagnose and resolve source-system, authentication, schema, and data-transfer issues.
- Develop reusable ingestion patterns and shared components to reduce duplication and improve maintainability across integrations
- Data Modeling & Transformation
- Develop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers.
- Translate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications.
- Evaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales.
- Maintain clear lineage and consistent modeling conventions across the warehouse.
- Data Modeling & Transformation
- Develop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers.
- Translate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications.
- Evaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales.
- Maintain clear lineage and consistent modeling conventions across the warehouse.
- Orchestration & Pipeline Operations
- Build and maintain orchestration across ingestion, data movement, transformation, validation, and delivery workflows.
- Manage dependencies, scheduling, retries, failure handling, and recovery across multi-stage pipelines.
- Troubleshoot failed or degraded production jobs and perform root-cause analysis.
- Improve pipeline reliability, runtime, scalability, and operational simplicity over time.
- Develop automation that reduces manual intervention and supports reliable day-to-day operation of the platform.
- Data Quality, Testing & Validation
- Design and implement data-quality checks across all pipeline stages: ingestion to Blob, Blob to SQL, SQL to dbt models, and dbt to downstream outputs.
- Build and maintain dbt tests
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