Resident Solution Architect (Databricks)
New
J
JobgetherData Engineering
Flexibility to work from anywhere in North AmericaFull-TimeSenior
SalaryUp to $80/hour on W2
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Job Details
- Experience
- 10+ years of consulting experience, including at least 7 years focused on data engineering, data platforms, analytics, or closely related disciplines.
- Required Skills
- AWSGCPAzureData engineeringSparkCI/CDDatabricksMLOps
Requirements
- 10+ years of consulting experience, including at least 7 years focused on data engineering, data platforms, analytics, or closely related disciplines.
- Proven hands-on delivery of approximately 6–8 or more enterprise-scale Databricks implementation projects.
- Deep expertise in Apache Spark and distributed computing, including a strong understanding of Spark runtime internals.
- Extensive knowledge of the Databricks Lakehouse Platform, its capabilities, architectural patterns, and current best practices.
- Databricks Data Engineering Professional Certification is strongly preferred.
- Demonstrated experience optimizing and tuning large-scale data platforms for performance, reliability, and scalability.
- Hands-on experience with at least one major cloud platform, including AWS, Azure, or GCP; multi-cloud experience is an advantage.
- Solid understanding of CI/CD practices and pipelines for production data-platform deployments.
- Working knowledge of MLOps concepts and their application across modern data and machine learning workflows.
- Strong consulting, communication, and client-facing skills, with the ability to explain complex technical concepts and influence architectural decisions.
- Ability to work independently in hands-on technical environments while balancing delivery priorities and client advisory responsibilities.
Responsibilities
- Lead end-to-end Databricks implementation initiatives, taking solutions from architecture and design through deployment and production.
- Advise enterprise clients on Lakehouse architecture, platform capabilities, best practices, technology roadmaps, and implementation strategies.
- Design scalable and resilient data platforms capable of supporting complex enterprise workloads and evolving business requirements.
- Optimize large-scale distributed data systems, identifying and resolving performance and scalability challenges across production environments.
- Serve as a hands-on technical lead, contributing directly to cloud-based implementations across AWS, Azure, and/or GCP.
- Design, implement, and support CI/CD pipelines that enable reliable and repeatable production deployments for data platforms.
- Apply MLOps principles and patterns to connect data engineering workflows with machine learning development and deployment processes.
- Collaborate closely with client technical teams and stakeholders, providing architectural guidance and helping drive successful adoption of modern Lakehouse practices.
- Bring practical expertise from multiple enterprise implementations to solve complex technical challenges and establish scalable architectural patterns.
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