Why This Role Stands Out
This hybrid role offers a fantastic opportunity to architect and implement cutting-edge enterprise-scale data and AI solutions on the Databricks platform, driving significant business outcomes. You'll thrive here if you possess deep expertise in Databricks, Spark, and cloud technologies, with a passion for hands-on problem-solving and customer collaboration. Apply now to leverage your extensive experience and shape the future of data innovation.
Quick Overview
Job Description
Role : Databricks Architect
Location: Atlanta/New Jersey
Note :
- Databricks Data Engineer Professional Certification (Mandatory)
- Should have worked as DataBricks Architect for at least 5 years.
Role Overview
We are seeking a highly experienced Senior Forward Deployed Engineer (FDE) / Resident Solutions Architect to work directly with customers in designing, implementing, and optimizing enterprise-scale data and AI solutions on the Databricks platform. This is a highly customer-facing consulting role that combines solution architecture, hands-on engineering, platform optimization, and strategic advisory responsibilities. The ideal candidate will have deep expertise in Databricks, Spark, Data Engineering, AI/ML, MLOps, and Cloud technologies, with a proven track record of delivering business outcomes and driving end-to-end ownership of large-scale data initiatives.
Experience
- 10+ years of consulting and customer-facing technology experience.
- 7+ years of experience in Data Engineering, Data Platforms, Analytics, or Big Data ecosystems.
- Experience delivering enterprise-scale Databricks implementations with significant hands-on involvement.
- Proven experience owning projects from solution design through production deployment and optimization.
- Deep hands-on expertise with Databricks Lakehouse Platform.
- Strong experience building data engineering solutions on Databricks.
- Expertise in Delta Lake, Unity Catalog, Workflows, SQL Warehouses, and platform governance.
- Expert-level knowledge of Apache Spark.
- Working knowledge of AI/ML lifecycle management.
- Experience with MLOps practices, model deployment, monitoring, and governance.
- Familiarity with productionizing machine learning workloads.
- Strong experience with AWS, Azure, or Google Cloud Platform.
- Experience implementing CI/CD pipelines for production deployments.
- Familiarity with Infrastructure as Code (IaC) and automation frameworks.
Preferred Qualifications
- Databricks Data Engineer Professional Certification (Mandatory/Strongly Preferred).
- Additional Databricks certifications in Data Engineering, Machine Learning, or Platform Administration.
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