AI Data Engineer

dariohealth

Gurugram 4 Years Exp Posted 3h ago

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:
  1. Own the domain roadmap
  2. Drive technical initiatives
  3. Support stakeholders
  4. Present progress and business impact
  5. Perform structured knowledge handoffs

 

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