RRemoteville
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Senior AI-First Data Engineer

Skills
Software As A ServiceAdoptionBusiness IntelligenceCustomer SuccessData AnalyticsDatabasesDecision Support
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • 6+ years of experience in Data Engineering Analytics Engineering or a related field
  • Experience designing and operating modern cloud data platforms
  • Experience in high-growth SaaS environments
  • Experience working with technical and business stakeholders
  • Ability to lead complex projects from design through delivery
  • Strong hands-on experience with Databricks
  • Strong hands-on experience with dbt
  • Strong hands-on experience with Airflow
  • Strong hands-on experience with Python for data engineering
  • Strong hands-on experience with SQL and data modeling
  • Strong hands-on experience with data warehousing architectures
  • Strong hands-on experience with data quality frameworks
  • Strong hands-on experience with data governance practices
  • Strong hands-on experience with data orchestration and integration
  • Experience with streaming architectures and event-driven systems
  • Experience with business intelligence platforms
  • Experience with product analytics platforms
  • Experience with CRM and customer data platforms
  • Ability to demonstrate AI-powered development workflows
  • Ability to automate repetitive engineering tasks using AI
  • Ability to use AI for debugging testing architecture exploration and documentation
  • Experience integrating AI into data or analytics workflows
  • Curiosity about agentic systems and AI-enabled data platforms
  • Evidence of measurable productivity gains through AI adoption

Nice to have

  • Semantic layers
  • RAG systems
  • Vector databases
  • Knowledge graphs
  • AI-native data products

Day to day

  • Build an AI-native data platform that makes business data discoverable actionable and consumable by humans and AI agents
  • Design and operate cloud-native streaming-first data systems that replace legacy batch-oriented architectures
  • Create data products semantic layers and metadata structures that support self-service analytics intelligent automation and agent-driven decision support
  • Improve reliability observability governance and performance across the data stack while partnering with Data Product Engineering GTM and AI teams
  • Use AI-powered workflows to accelerate development testing documentation monitoring and operational work across the data organization
  • Lead technical standards mentorship and cross-functional alignment to raise the engineering bar and drive business impact