Job Title: Senior Databricks Solutions Architect (Freelance Talent Pool)
Location: Brazil (Remote)
Engagement: Contractor / Freelancer
Workload: Project-based, typically part-time
Start Date: Ongoing / based on project availability
Job Description:
We are building a freelancer talent pool of experienced Senior Databricks Solutions Architects as part of a Databricks DPP program.
This opportunity is not currently tied to a specific project. Selected professionals will complete the qualification process and may be matched with future Databricks projects according to their expertise, availability, project scope, and duration.
Engagements are contractor/freelance and typically part-time, depending on project requirements.
Important: Joining the talent pool does not guarantee an immediate project assignment.
Key Responsibilities:
- Design scalable enterprise data solutions using Databricks and Apache Spark.
- Provide hands-on architecture and technical guidance across Databricks implementations.
- Support the design and delivery of production-grade Databricks solutions across multiple cloud environments.
- Lead or participate in architecture discussions, technical discovery sessions, and solution design activities with clients.
- Advise on Databricks performance optimization, scalability, governance, and platform best practices.
- Support CI/CD, production deployment, and MLOps practices within Databricks environments.
- Collaborate with client stakeholders, architects, and engineering teams throughout project engagements.
- Participate in technical assessments and the Databricks vetting process required for the DPP talent pool.
Required Qualifications:
- 7+ years of experience in Data Engineering, Data Platforms, and Analytics.
- 10+ years of consulting experience.
- Experience delivering 6–8+ Databricks projects with hands-on development responsibilities.
- Databricks Data Engineering Professional certification and required Databricks training completed.
- Advanced knowledge of distributed computing with Apache Spark, including Spark runtime internals.
- Experience across at least two cloud ecosystems (AWS, Azure, GCP), with deep expertise in at least one.
- Strong understanding of the Databricks platform, performance optimization, scalability, and production architecture.
- Familiarity with CI/CD pipelines and production deployments.
- Working knowledge of MLOps practices.
- AI/GenAI knowledge is considered a plus, not a requirement.
Key Screening Filters Before Submission:
- 7+ years Data Engineering / Data Platforms / Analytics.
- 10+ years consulting experience.
- 6–8+ hands-on Databricks projects.
- Databricks Data Engineering Professional certification + required training.
- Advanced Spark / distributed computing knowledge.
- Experience with at least 2 clouds.
- CI/CD + production deployments.
- Working MLOps knowledge.
- Strong client-facing / consulting capabilities.
Engagement Model:
- Freelancer talent pool / project-based / typically part-time.
- There is currently no specific project or guaranteed immediate assignment.
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