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Databricks Data Engineer

Central Business Solutions Inc
United States
6 days ago
Apply on www.dice.com
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
6 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Amazon Web Services Data Analysis Microsoft Azure Continuous Integration Information Engineering Distributed Systems Software Deployment Google Cloud Cloud Platform System Apache Spark Data Management Machine Learning Operations
+1 more
Databricks

Requirements

*7+ years experience in Data Engineering, Data Platforms & Analytics, 10+ years of consulting experience. *Completed Data Engineering Professional certification & required classes. *Minimum 6-8+ projects delivered with hands-on experience in development on Databricks. *Working knowledge of two or more common Cloud ecosystems (AWS, Azure, Google Cloud Platform) with deep expertise in at least one. *Deep experience with distributed computing with Spark with knowledge of Spark runtime internals *Familiarity with CI/CD for production deployments. *Working knowledge of MLOps. *Current knowledge across the breadth of Databricks product and platform features. *Familiarity with optimizations for performance and scalability.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

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Viktoria Semaan Viktoria Semaan · World Congress 2026 Europe

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Key takeaways and accessing the Databricks developer toolkit

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LIVE • almost 6 years ago

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World Congress 2026 Europe • about 1 month ago

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World Congress 2024 • almost 2 years ago

The Cloud is Calling: Answer with In-Demand Skills

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Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source thumbnail
29:50
World Congress 2026 Europe • about 1 month ago

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source

Viktoria Semaan

OLTP in the Lakehouse: Redefining Data for AI Workloads thumbnail
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Europe 2026 Virtual • about 2 months ago

OLTP in the Lakehouse: Redefining Data for AI Workloads

Oleksandra Bovkun

Let's Get Aggregated: Custom UDAFs in Spark thumbnail
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World Congress 2025 • 12 months ago

Let's Get Aggregated: Custom UDAFs in Spark

Nikolai Nikolaev

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