AI Data Engineer
dariohealth
Job Description
End-to-End Data Solution Ownership
- Own the full lifecycle of data solutions including ingestion, transformation, modeling, visualization, automation, monitoring, and stakeholder delivery.
- Design, develop, and maintain scalable ETL/ELT pipelines, data warehouses, semantic layers, and near real-time data flows.
- Build and maintain dashboards, operational reporting, KPI tracking, and business-facing analytical products.
- Collaborate closely with Product, Design, R&D, Analytics, Marketing, Operations, and Business stakeholders to translate requirements into scalable technical solutions.
- Drive initiatives independently from planning and architecture through deployment and operational support.
AI-First Engineering & Automation
- Leverage AI-assisted development workflows to accelerate execution, improve code quality, and optimize operational efficiency.
- Design and maintain AI-enabled workflows including prompt pipelines, automated enrichment processes, vector integrations, and LLM-assisted tooling where applicable.
- Evaluate and adopt emerging AI/GenAI technologies to improve engineering velocity and organizational scalability.
- Contribute to automation-first engineering practices across development, QA, monitoring, and documentation workflows.
- Utilize Git-based workflows and modern software engineering practices as part of daily execution.
Data Engineering & Infrastructure
- Develop scalable and reliable data pipelines using modern orchestration and transformation frameworks.
- Optimize existing infrastructure for performance, cost efficiency, scalability, and reliability.
- Build and maintain data quality validation frameworks, monitoring systems, and operational alerting processes.
- Support both batch and near real-time processing architectures.
- Contribute to future-state architecture decisions across data platform and AI infrastructure initiatives.
- Maintain technical documentation including architecture diagrams, data dictionaries, SOPs, and operational playbooks.
Analytics & Business Partnership
- Partner directly with business stakeholders to understand operational challenges and identify opportunities for automation and insight generation.
- Own business-critical KPIs, reports, and dashboards.
- Surface actionable insights proactively to support strategic and operational decision-making.
- Support experimentation frameworks, metric validation, and analytical initiatives.
- Ensure alignment between business expectations, data definitions, and technical implementation.
Domain Ownership & Rotation Model
- Operate within a rotating domain ownership structure designed to increase business understanding and reduce organizational silos.
- During each rotation cycle:
- Own the domain roadmap
- Drive technical initiatives
- Support stakeholders
- Present progress and business impact
- Perform structured knowledge handoffs