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Staff Data Engineer (Python, LLM, Data Platforms) - Remote

Worldwide Salaried Open

Staff Data Engineer (Python, LLM, Data Platforms) – Remote Remote (U.S. Only) $220,000 – $240,000 + Full Benefits About the Opportunity An international media and digital analytics organization is seeking a Staff Data Engineer (formally titled Staff Engineer – Data Engineering) to lead the evolution of its large-scale data platforms. This is a senior, high-impact individual contributor role focused on data architecture, cross-platform integration, and building LLM-powered data products. You’ll work across multiple systems and teams to define long-term architecture and ensure platforms scale reliably in a complex environment. This is a data engineering and platform architecture role—not an ML model development position. Experience in streaming platforms (e.g., Spotify, Netflix, Hulu) or B2B SaaS environments is strongly preferred.

Key Responsibilities

  • Design and own scalable, cross-platform data architecture across complex systems
  • Lead integration of data platforms to enable unified insights and product capabilities
  • Build and optimize large-scale data pipelines (batch and streaming) and data models using Python and modern frameworks
  • Develop production-grade Python code for data processing, platform services, and LLM-enabled workflows
  • Design and deliver LLM-powered applications and data products
  • Establish best practices for data modeling, pipeline design, and system observability
  • Mentor engineers and raise the technical bar across teams
  • Translate business needs into clear, scalable architectural decisions
  • Contribute to architecture reviews, roadmap planning, and on-call support

Required Qualifications

  • 8+ years of experience in data engineering or data architecture
  • Proven experience working in complex, multi-system or multi-team environments
  • Strong Python expertise (5+ years of hands-on, production-level development)
  • Experience building LLM-powered systems or data-driven AI applications in production
  • Deep experience with large-scale data architecture (pipelines, modeling, warehousing)
  • Strong communication skills — able to clearly articulate architectural decisions
  • Demonstrated success mentoring engineers and elevating team performance

Strongly Preferred

  • Experience in streaming platforms or B2B SaaS environments
  • Experience with Snowflake, BigQuery, or similar cloud data warehouses
  • Familiarity with Kafka or real-time streaming architecture
  • Experience with Kubernetes or containerized environments
  • Track record of cross-platform or cross-division data integration
  • Passion for mentorship, teaching, and building engineering culture

Technical Environment

  • Cloud data warehouses (Snowflake, BigQuery, or similar)
  • Data processing & orchestration (Spark, Airflow)
  • Streaming platforms (Kafka or equivalent)
  • Batch and real-time data pipelines
  • Cloud-native / containerized environments (Kubernetes a plus)

What Success Looks Like

  • First 90 days: Deliver an architectural assessment and identify integration opportunities
  • 6 months: Lead a cross-platform data or LLM-driven initiative
  • 12 months: Own the data architecture roadmap and mentor multiple engineers

Benefits

  • Medical, Dental, and Vision coverage
  • 401(k) with company match
  • Generous paid parental leave
  • Flexible work schedule + unlimited PTO
  • Monthly stipends (home office, internet, wellness)
  • Annual company event
  • Collaborative culture with strong work-life balance

Remote Skills: Architectural Analysis, Architectural Services, Artificial Intelligence (AI), Best Practices, Business-to-Business (B2B), Cloud Computing, Communication Skills, Data Management, Data Modeling, Data Processing, Data Warehousing, Mentoring, Multiplatform/Cross-Platform, On Call, Python Programming/Scripting Language, Snowflake Schema, Software Engineering, Software as a Service (SaaS), Team Player, Warehousing About the Company: Search Partner Group Apply tot his job Apply To this Job

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