Principal Architect - Databricks

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Job Summary

We are expanding our Databricks capability within the Data and AI practice and are looking for a Principal Solutions Architect to lead the technical side of it.

You will work directly with customers from the outset, qualifying opportunities, conducting technical discovery with customer architects and data leaders, designing target-state platforms, and owning the technical win. Much of the work ahead involves platform build-out, migration, and consolidation, helping enterprises move to the Databricks Lakehouse from Hadoop and Spark estates, legacy analytics platforms, established data warehouse and appliance environments, and other cloud platforms. The role also supports AI and machine learning initiatives that Databricks is designed to enable.

This is a senior individual contributor position with significant influence over the solutions we build and bring to market. It is ideal for someone who wants to shape a capability rather than operate within one that is already defined.

Role Description

Own the Technical Side of Databricks Pursuits

Lead qualification, technical discovery, target architecture design, effort estimation, and risk assessment.

Develop technical content for proposals and Statements of Work (SOWs).

Build and maintain technical relationships with customer architects and stakeholders.

Provide customers with realistic and accurate assessments of migration complexity, implementation effort, AI workload readiness, and associated risks.

Provide Architectural Oversight Through Delivery

Review architecture decisions throughout project delivery.

Confirm that the delivered solution aligns with what was originally scoped and proposed.

Surface technical risks and issues early in the engagement.

Remain closely involved throughout delivery to ensure accountability for architecture and outcomes.

Shape Databricks Service Offerings

Define the technical content of market-facing Databricks offerings.

Establish what is included in scope, how success is measured, and what evidence validates successful completion.

Create repeatable delivery standards and frameworks that teams can execute consistently.

Partner with Databricks

Engage directly with Databricks field teams and specialist architects on joint customer accounts.

Build productive relationships with Databricks technical teams.

Effectively collaborate and navigate technical challenges, feedback, and differing viewpoints from vendor architects and specialists.

Behaviors and Competencies

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.

Self-Development: Can demonstrate a commitment to continuous learning and adaptability to new ideas and methods.

Leadership: Can take ownership of complex team initiatives, collaborate with others in decision-making processes, and drive team performance.

Strategic Thinking: Can analyze complex situations, anticipate future trends, and align and integrate strategies across departments or functions.

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.

Analytical Thinking: Can use advanced analytical techniques to solve complex problems, draw insights, and communicate the solutions effectively.

Prioritization: Can take ownership of complex task management, collaborate with others to align priorities, and drive team efficiency.

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.

Organization: Can oversee complex projects with multiple moving parts, ensure team alignment with organizational systems, and adapt to changing priorities.

Communication: Can effectively communicate complex ideas and information to diverse audiences, facilitate effective communication between others, and mentor others in effective communication.

Interpersonal Skills: Can communicate effectively, build relationships, resolve conflicts, influence others, and support others in developing their interpersonal skills in major situations.

Skill Level Requirements

Lakehouse Platform and Architecture

Account and workspace design

Unity Catalog and metastore architecture

Compute strategy across interactive, jobs, and serverless workloads

Open table format decisions

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

Migrating organizations from Hadoop and Spark environments

Modernizing legacy analytics and statistical platforms

Consolidating established warehouses and appliance-based solutions

Migrating workloads from other cloud platforms

Key areas of expertise include:

Platform assessments

Wave planning and migration strategy

Workload and code conversion

Reconciliation and parity validation

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

Batch data ingestion

Streaming data ingestion

Declarative pipeline development

Workflow orchestration

Testing and validation frameworks

Data quality instrumentation

Building and operating reliable, production-ready data engineering solutions

Machine Learning and AI Engineering

Feature engineering

Experiment tracking

Model registry and serving

Evaluation and validation frameworks

Retrieval and grounding architectures

Agent development

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

Catalog-based access controls

Data classification

Data masking

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.

Consumption Economics

Compute sizing strategies

Workload placement optimization

Serverless versus classic compute trade-offs

Consumption attribution and chargeback models

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:

Deep expertise in lakehouse architecture and platform design

Deep expertise in migration and consolidation

Deep expertise in data engineering

Sufficient proficiency in the remaining areas to recogn