Principal Data Architect

4 days ago

Melbourne, Victoria, Australia beBeeDataArchitect Full time $130,000 - $180,000

We foster innovation by bridging AI with human capabilities. Our mission is to enable organizations to scale results without exponential headcount growth, leveraging AI and automation.

This role aligns with our employee archetype The Engineer. The Engineer crafts robust, scalable, cost-effective solutions, embeds guardrails, and measures impact end-to-end.

Key Responsibilities
  • Ai Strategy Development: Develop and execute AI strategies to achieve business objectives and expand AI capabilities.
  • Data Engineering Requirements: Collaborate with stakeholders to understand objectives and translate them into data-engineering requirements.
  • Communication: Communicate architecture decisions, trade-offs, and roadmap progress in clear, compelling language.
  • Trend Analysis: Stay updated on industry trends, competitive landscape, and emerging technologies to inform AI strategies.
Change Enablement
  • Project Management: Leads end-to-end project management for data engineering initiatives, including scoping, planning, stakeholder communication, and delivery of outcomes aligned to business objectives.
  • Stakeholder Management: Manage multiple commercial and technical stakeholders effectively.
  • Team Leadership: Mentor junior engineers, championing best practices in code reviews, CI/CD, and documentation.
  • Adaptability: Adapt to changing toolkits, models, and scopes, and manage volatile POCs and prototypes.
  • Collaboration: Work closely with the sales, marketing, product, and delivery teams to align AI efforts with overall business objectives.
  • Team Growth: Support the expansion of the team as we grow.
Ai Orchestration
  • Solution Architecture: Design end-to-end systems that combine models, workflows, automation, and APIs.
  • Technical Leadership: Build data ingestion, transformation, and feature-store pipelines that power LLMs and ML models in production.
  • Monitoring: Embed automated data-quality tests, monitoring, and cost-controls to keep workloads safe and efficient.
  • Use Case Delivery: Lead the development and deployment of AI initiatives that address real-world business needs.
  • Automation Integration: Combine AI models with workflow automation and decision systems to streamline processes.
Technical Versatility
  • Tool Fluency: Design and implement batch and streaming ELT/ETL workflows using Python, SQL, dbt, Airflow, Spark, Snowflake, Databricks, or similar technologies.
  • Stack Agility: Adapt quickly across cloud, open-source, and low-code/no-code toolchains to meet solution needs.
  • Integration Skills: Integrate diverse data sources (APIs, events, files, warehouses) and optimise storage and compute across cloud environments.
  • Landscape Awareness: Continuously research, evaluate new technologies and integrate promising tools into the delivery toolkit.
Engineering Mindset
  • Data Engineering: Designs, builds, and maintains scalable data pipelines, robust data models, and production-grade machine learning solutions.
  • Ai Scalability: Introduce repeatable and scalable AI processes.

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