Lead Databricks Architect

Nimble Gravity

11d

About The Position

Do you want to design and scale enterprise data platforms that power analytics, AI, and real business impact? Ready to lead architectural decisions across complex, high-impact data environments? Then this role is for you. At Nimble Gravity, we turn complex data into actionable insight—and that starts with building scalable, high-performing data platforms. We’re seeking a Lead Databricks Architect who is passionate about designing and operating modern data ecosystems, driving platform excellence, and enabling organizations to unlock the full value of their data. If you thrive at the intersection of data architecture, platform ownership, and hands-on technical execution, and enjoy leading high-impact initiatives in complex environments, then you belong with us.

Requirements

Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related fields.
10+ years of experience in data architecture, data engineering, or enterprise analytics.
Proven experience designing and operating modern, cloud-native data platforms.
Hands-on expertise with AWS-based data ecosystems and modern analytics stacks.
Strong experience building data platforms that support AI, machine learning, and advanced analytics.
Experience operating in federated data models and complex enterprise environments.
Strong hands-on experience with Databricks, including workspace setup, configuration, administration, and platform optimization; experience with AWS setup and administration is strongly preferred.
Deep technical fluency combined with strong business acumen.
Ability to translate strategy into executable architectural designs.
Pragmatic, delivery-oriented mindset with strong attention to data quality and sustainability.
Collaborative, trusted partner to senior client stakeholders and technical teams.
Comfortable operating in high-visibility, transformational initiatives.

Nice To Haves

Cloud or data architecture certifications (AWS, data management, analytics) preferred.

Responsibilities

Act as the senior technical authority for enterprise data architecture across client engagements.
Design and evolve cloud-native, best-of-breed data platforms for our clients, supporting analytics, reporting, automation, and AI at enterprise scale, with a strong focus on Databricks as a core platform.
Define target-state architectures for data ingestion, transformation, storage, semantic layers, and consumption across federated domains.
Establish and enforce architectural standards for scalability, performance, security, resiliency, and cost efficiency, including platform-level governance and operational best practices.
Architect and guide the implementation of modern data pipelines using tools such as Databricks and dbt.
Design ELT and streaming patterns that are testable, observable, and resilient.
Define data modeling standards (analytical, dimensional, and semantic) that enable trusted self-service analytics.
Partner with client data engineering teams to ensure consistent application of patterns and reuse of shared platform capabilities, including effective use and management of Databricks environments.
Design AI-ready data foundations, including semantic layers, contextual metadata, and reusable data contracts.
Enable machine learning and generative AI use cases by ensuring data is well-modeled, discoverable, governed, and explainable.
Collaborate with client Data Science and Analytics teams to support feature stores, experimentation environments, and reusable data products.
Advance semantic modeling and context engineering to enable natural-language analytics and AI-driven insights.
Partner with client Data Governance stakeholders to define standards for data quality, metadata management, lineage, and stewardship.
Ensure architectural designs support regulatory and compliance requirements (e.g., SOX, SEC, FINRA, GDPR, DORA).
Promote reuse, interoperability, and consistent definitions of critical enterprise data across client environments, including alignment with platform governance models.
Own and administer Databricks workspaces across client environments, including configuration, access controls, and governance (Unity Catalog, IAM, RBAC).
Manage cluster configurations, job orchestration, and cost optimization to ensure efficient and reliable platform operations.
Monitor platform performance, troubleshoot issues, and ensure SLAs for data pipelines and workloads.
Establish best practices for CI/CD, environment management, and operational support within Databricks.
Partner with client teams to operationalize and scale Databricks as a core enterprise data platform.