Databricks Solution Architect
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Job Description
Databricks Solution Architect designing enterprise Lakehouse and AI platforms for Bits In Glass. Building prototypes, guiding customers, and shaping technical strategies across Canada.
Responsibilities:
- Design scalable Databricks solution architectures using Lakehouse, Delta Lake, and Unity Catalog patterns
- Lead technical discovery workshops covering customer objectives, data challenges, and current-state architecture
- Build working prototypes, proofs-of-concept, and reference architectures
- Guide customers on data pipeline, data modeling, and platform governance best practices
- Advise on integrating Databricks with AWS, Azure, GCP, and adjacent data tooling
- Demonstrate Databricks capabilities across Data Engineering, Data Science, ML, and Generative AI use cases, including Mosaic AI, MLflow, and Feature Store
- Identify and mitigate technical risks to support delivery teams
- Translate complex technical concepts into business value for technical stakeholders and executives
- Partner with account teams throughout the sales process to shape technical strategies
- Collaborate with Data Scientists and ML Engineers on AI-powered demo assets and reference architectures
- Serve as a resident Databricks expert and enable the broader team
- Contribute reusable accelerators, demo assets, and technical playbooks
- Stay current on Databricks releases and evolving data and AI patterns
- Represent BIG at customer events, webinars, and Databricks partner activities
- Provide field feedback to Databricks product and partner teams
- Travel up to 15% for customer meetings and partner collaboration
Requirements:
- 3+ years of hands-on experience with Databricks and/or Snowflake in a technical capacity
- 5+ years in customer-facing technical roles — solutions architecture, technical consulting, or sales engineering
- Experience designing, presenting, and delivering production data architectures for enterprise customers, including Lakehouse, Delta Lake, and Unity Catalog patterns, on AWS, Azure, and/or GCP
- Databricks Professional-level certification, such as Data Engineer Professional or Machine Learning Professional
- Strong expertise in at least one core data domain: big data engineering (Spark, Kafka), Data Warehousing & ETL, or Data Science & ML
- Fluency in Python and SQL
- Exceptional verbal and written communication and presentation skills
- Ability to engage in and lead business-level meetings with technical and non-technical client and internal team members
- Ability to translate complex topics into clear business value and earn buy-in from engineers and executives
- Nice to have: a degree in Computer Science, Applied Mathematics, Operations Research, or a related quantitative field
- Nice to have: experience with dbt, Fivetran, Airflow, or Delta Sharing
- Nice to have: familiarity with AI/GenAI frameworks and LLM application patterns
- Nice to have: Databricks GenAI Engineer Associate certification
- Nice to have: exposure to enterprise engagement cycles and how technical decisions shape deal outcomes
- Nice to have: experience with Frontier LLMs such as Claude Code for SDLC acceleration
Benefits:
- Recognized globally as a Great Place to Work
- Access to 10+ cutting-edge technology practices and continuous learning opportunities
- Competitive compensation and benefits
- Flexible, remote-friendly work environment
- Meaningful, high-impact data and AI projects for enterprise clients
- Team that celebrates wins together
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