RandomTrees
Databricks Architect
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Databricks Architect on a contract basis, requiring expertise in Databricks, PySpark, Spark SQL, and data pipeline architecture on AWS, Azure, or GCP. Strong Python and SQL skills, along with AI/ML knowledge, are essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 23, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Palm Beach County, FL
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🧠 - Skills detailed
#Delta Lake #Spark SQL #Synapse #Azure #AWS S3 (Amazon Simple Storage Service) #GitHub #GCP (Google Cloud Platform) #Databricks #BigQuery #PySpark #AI (Artificial Intelligence) #AWS (Amazon Web Services) #Redshift #ChatGPT #Spark (Apache Spark) #ADLS (Azure Data Lake Storage) #ML (Machine Learning) #Dataflow #ADF (Azure Data Factory) #SQL (Structured Query Language) #Data Pipeline #S3 (Amazon Simple Storage Service) #Data Modeling #Python
Role description
Technical Skills Required.
Experience in Databricks platform
Deep hands-on expertise with PySpark, Spark SQL, Delta Lake, and Databricks Workflows
Proven experience architecting large-scale data pipelines on at least one major hyperscaler — AWS (S3, Glue, Redshift, Kinesis, EMR), Azure (ADF, ADLS, Synapse, Event Hubs), or GCP (Dataflow, BigQuery, Pub/Sub)
Python and SQL — must be able to write and review code.
Strong experience in data modeling, dimensional modeling, and lakehouse design patterns
Demonstrated awareness of the evolving AI landscape — including LLMs, generative AI, AI coding assistants, and enterprise AI platforms
Practical, hands-on experience using AI tools (e.g., GitHub Copilot, ChatGPT, Claude, Cursor, or similar) to improve personal and team productivity
AI/ML capabilities — understanding how data assets enable AI-driven products and insights
Technical Skills Required.
Experience in Databricks platform
Deep hands-on expertise with PySpark, Spark SQL, Delta Lake, and Databricks Workflows
Proven experience architecting large-scale data pipelines on at least one major hyperscaler — AWS (S3, Glue, Redshift, Kinesis, EMR), Azure (ADF, ADLS, Synapse, Event Hubs), or GCP (Dataflow, BigQuery, Pub/Sub)
Python and SQL — must be able to write and review code.
Strong experience in data modeling, dimensional modeling, and lakehouse design patterns
Demonstrated awareness of the evolving AI landscape — including LLMs, generative AI, AI coding assistants, and enterprise AI platforms
Practical, hands-on experience using AI tools (e.g., GitHub Copilot, ChatGPT, Claude, Cursor, or similar) to improve personal and team productivity
AI/ML capabilities — understanding how data assets enable AI-driven products and insights
