Behaviors and Competencies
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Willingness to Learn: Can apply new learning to daily work, encourage and facilitate learning in others, and actively make changes to work based on feedback.
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Self-Development: Can demonstrate a commitment to continuous learning and adaptability to new ideas and methods.
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Leadership: Can take ownership of complex team initiatives, collaborate with others in decision-making processes, and drive team performance.
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Strategic Thinking: Can analyze complex situations, anticipate future trends, and align and integrate strategies across departments or functions.
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Problem-Solving: Can proactively identify and take ownership of complex problem-solving initiatives, initiate preventative measures, collaborate with others to find solutions, and drive successful outcomes.
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Analytical Thinking: Can use advanced analytical techniques to solve complex problems, draw insights, and communicate the solutions effectively.
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Prioritization: Can take ownership of complex task management, collaborate with others to align priorities, and drive team efficiency.
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Customer-Centric Mindset: Can take ownership of customer-centric initiatives, ensuring products and services align with customer needs. Collaborates with cross-functional teams to integrate customer feedback into product development.
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Organization: Can oversee complex projects with multiple moving parts, ensure team alignment with organizational systems, and adapt to changing priorities.
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Communication: Can effectively communicate complex ideas and information to diverse audiences, facilitate effective communication between others, and mentor others in effective communication.
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Interpersonal Skills: Can communicate effectively, build relationships, resolve conflicts, influence others, and support others in developing their interpersonal skills in major situations.
Lakehouse Platform and Architecture
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Account and workspace design
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Unity Catalog and metastore architecture
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Compute strategy across interactive, jobs, and serverless workloads
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Open table format decisions
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Cost architecture and design choices that affect long-term scalability and affordability
The ability to design platforms that remain technically and financially sustainable at enterprise scale is critical.
Migration and Consolidation
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Migrating organizations from Hadoop and Spark environments
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Modernizing legacy analytics and statistical platforms
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Consolidating established warehouses and appliance-based solutions
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Migrating workloads from other cloud platforms
Key areas of expertise include:
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Wave planning and migration strategy
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Workload and code conversion
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Reconciliation and parity validation
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Cutover planning and execution
This is expected to represent a significant portion of the work within the practice and requires genuine expertise rather than general familiarity.
Data Engineering and Pipelines
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Declarative pipeline development
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Testing and validation frameworks
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Data quality instrumentation
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Building and operating reliable, production-ready data engineering solutions
Machine Learning and AI Engineering
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Model registry and serving
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Evaluation and validation frameworks
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Retrieval and grounding architectures
This represents one of Databricks’ key differentiators and is an area where many customers require assistance moving from proof-of-concept solutions to production-grade, supportable implementations.
Governance at the Perimeter
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Catalog-based access controls
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Data lineage and governance frameworks
Candidates should understand how Databricks governance integrates with enterprise governance strategies and be able to address challenges that exist across organizational and platform boundaries.
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Compute sizing strategies
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Workload placement optimization
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Serverless versus classic compute trade-offs
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Consumption attribution and chargeback models
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Cost management and optimization practices
Candidates must be comfortable discussing platform costs with customers and providing practical guidance on controlling and forecasting consumption.
Technical Depth Expectations
Candidates do not need to be equally strong across all six areas; however, they must possess:
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Deep expertise in lakehouse architecture and platform design
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Deep expertise in migration and consolidation
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Deep expertise in data engineering
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Sufficient proficiency in the remaining areas to recognize when specialist support should be engaged
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Substantial hands-on Databricks experience gained through:
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A Databricks partner organization
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Managing a significant Databricks Lakehouse environment internally
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This role requires Databricks-specific expertise rather than general data platform leadership experience.
Enterprise Migration and Consolidation Experience
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Proven success delivering enterprise-scale migration and consolidation initiatives.
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Ability to discuss at least one major migration project in detail, including lessons learned and outcomes.
Production Machine Learning and AI Experience
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Experience operating and supporting production machine learning or AI solutions.
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Expertise beyond proof-of-concept implementations.
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Demonstrated understanding of the operational, governance, and maintenance considerations required for production AI workloads.
Principal-Level Technical Leadership
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Proven success operating at Principal Architect level or equivalent.
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Recognized as the senior technical authority in customer engagements.
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Trusted to commit organizations to scopes, architectures, and technical approaches.
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Experience in presales environments, or
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Delivery leadership experience demonstrating the ability to:
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Lead customer discussions
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Define scope and solution approaches
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Develop proposals and Statements of Work
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Stand behind technical commitments made during the sales process
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Comfortable working with and being challenged by vendor architects and technical specialists.