Why this role stands out
This client-facing role offers an exceptional opportunity to lead Databricks implementations and shape cloud-native data platforms, providing significant career growth and skill development. You'll thrive here if you are a seasoned data architect passionate about designing scalable solutions and advising clients, with the added flexibility of a remote work arrangement. Apply today to make a real impact with SysMind!
Quick overview
Job description
Location: San Francisco, CA
Work Arrangement: Remote, PST Hours
Employment Type: Contract
Duration: 12 months
Position Overview
We are seeking a Databricks Resident Solution Architect with extensive experience in data engineering, data platforms, analytics, and modern data warehouse solutions. This client-facing role will lead Databricks implementations, provide architecture guidance, and work directly with clients to design and deliver scalable cloud-native data platforms and Databricks solutions.
Key Responsibilities
- Lead end-to-end Databricks implementations as a hands-on developer, technical lead, or solution architect.
- Serve as a client-facing technical advisor, understanding business requirements and recommending appropriate Databricks solutions.
- Design and implement cloud-native data platforms across AWS, Azure, and/or GCP.
- Architect scalable ETL/ELT pipelines using Databricks, Delta Lake, Structured Streaming, and modern data engineering frameworks.
- Design and optimize large-scale distributed data processing workloads for performance, scalability, and cost efficiency.
- Apply advanced Apache Spark expertise, including partitioning, execution plans, memory management, and performance optimization.
- Implement DevOps and CI/CD practices using Azure DevOps, GitHub Actions, GitLab CI/CD, Jenkins, or similar tools.
- Apply MLOps principles for machine learning lifecycle management, model deployment, and monitoring.
- Leverage Databricks Lakehouse capabilities including Delta Lake, Unity Catalog, Workflows, MLflow, and Delta Live Tables.
- Troubleshoot complex data platform challenges and recommend architecture patterns aligned with business and technical requirements.
- Support client discussions and demonstrate the value and capabilities of Databricks solutions.
Required Qualifications
- 12–15+ years of experience in data engineering, data platforms, data analytics, and modern data warehouse solutions.
- 10+ years of consulting and client-facing delivery experience.
- Demonstrated experience delivering 6–8+ end-to-end Databricks implementations.
- Advanced Databricks certification, such as Databricks Data Engineering Professional certification, with completion of relevant learning paths and coursework.
- Strong expertise designing and implementing cloud-native data platforms across AWS, Azure, and/or GCP, with deep hands-on expertise in at least one cloud platform.
- Advanced knowledge of Apache Spark, including performance optimization, partitioning strategies, execution plans, memory management, and runtime internals.
- Extensive hands-on experience with Databricks, Delta Lake, Structured Streaming, and scalable ETL/ELT pipelines.
- Experience implementing production-grade DevOps and CI/CD for data solutions.
- Working knowledge of MLOps, machine learning lifecycle management, model deployment, and monitoring.
- Strong knowledge of the Databricks Lakehouse Platform and its core capabilities.
- Experience tuning large-scale distributed workloads and designing highly performant, scalable, and cost-efficient solutions.
- Strong troubleshooting and solution architecture skills.
Preferred Qualifications
- Databricks Solutions Architect Champion program participation or equivalent advanced Databricks expertise.
- Experience working with public sector organizations
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