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
  • Proven experience designing and operating modern cloud data platforms
  • Experience in high-growth SaaS environments
  • Experience working with both technical and business stakeholders
  • Ability to lead complex projects from design through delivery
  • Strong hands-on experience with Databricks dbt Airflow Python SQL data modeling data warehousing data quality data governance orchestration and integration
  • Experience with streaming architectures and event-driven systems
  • Experience with business intelligence platforms product analytics platforms CRM and customer data platforms
  • Ability to demonstrate AI-powered development workflows and automation of repetitive engineering tasks
  • Experience using 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

  • AI-native mindset
  • Streaming-first thinker
  • Strong cross-functional partner
  • High ownership
  • Technical mentor
  • Data governance focus
  • Self-service analytics mindset

Day to day

  • Build and evolve a cloud-native data platform that supports AI agents, analytics, and business operations.
  • Design and operate streaming-first data systems, improving reliability, observability, governance, and performance across the stack.
  • Create data products, semantic layers, and knowledge structures that make business data discoverable, actionable, and safe for both humans and AI agents.
  • Partner with Data Product Engineering GTM and AI teams to drive self-service analytics intelligent automation and faster decision support.
  • Use AI-powered workflows to accelerate development testing documentation monitoring and other day-to-day engineering tasks.