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Databricks Engineer Jobs in Cupertino, CA (NOW HIRING)

iLink Digital

Databricks Engineer

Milpitas, CA · On-site

Role: Databricks Engineer Experience 4-8 years Location As per business requirement Employment Type Full-time Job Summary We are looking for a skilled Databricks Engineer with strong expertise in ...

iLink Digital

Databricks Engineer

Milpitas, CA · On-site

$120 - $160/hr

Milpitas, United States | Posted on 07/22/2026 We are looking for a skilled Databricks Engineer with strong expertise in designing, developing, and optimizing modern data engineering solutions on the ...

Databricks

Sr Software Engineer- CXI

Mountain View, CA · On-site

$164K - $225K/yr

P-1640 At Databricks, we're passionate about enabling data teams to solve the world's toughest ... Founded by engineers and driven by customer obsession, Databricks takes pride in tackling hard ...

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All JobsDatabricks Engineer JobsDatabricks Engineer Jobs in Cupertino, CA

Databricks Engineer information

See Cupertino, CA salary details

$73.4K

$137.7K

$250.4K

How much do databricks engineer jobs pay per year?

As of Aug 24, 2026, the average yearly pay for databricks engineer in Cupertino, CA is $137,725.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,300.00 and $163,500.00 per year, depending on experience, location, and employer.

What is a Databricks engineer?

A Databricks Engineer is a data engineering professional who specializes in using the Databricks platform to build, manage, and optimize data pipelines and analytics solutions. They work with big data technologies like Apache Spark, Delta Lake, and cloud services to process and analyze large datasets efficiently. Their role often involves developing ETL (extract, transform, load) workflows, setting up data lakes, and ensuring data quality and performance for business intelligence and machine learning applications.

What are the key skills and qualifications needed to thrive as a Databricks engineer?

To thrive as a Databricks Engineer, you need strong expertise in big data processing, cloud platforms (like AWS or Azure), and proficiency with languages such as Python, SQL, and Scala, often supported by a degree in computer science or a related field. Familiarity with Apache Spark, Databricks Workspace, version control systems like Git, and relevant Databricks certifications are typically required. Strong analytical thinking, collaboration, and effective communication skills help you understand business needs and work seamlessly with data teams. These skills ensure efficient data pipeline development, scalable analytics solutions, and successful integration of Databricks into organizational workflows.

What are some common challenges faced by Databricks engineers when working with large-scale data pipelines?

Databricks Engineers often encounter challenges related to optimizing the performance and reliability of large-scale data pipelines. These can include efficiently managing cluster resources, handling data partitioning to prevent bottlenecks, and troubleshooting job failures due to resource constraints or data quality issues. Collaboration with data scientists, analysts, and DevOps teams is essential to ensure seamless integration and deployment of production workflows. Staying current with evolving Databricks features and best practices also plays a key role in overcoming these challenges.

How much does a Databricks engineer make?

A Databricks engineer's salary typically ranges from $90,000 to $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized skills in Spark, cloud platforms, and data engineering can earn higher compensation, often including bonuses and benefits.

Is a Databricks engineer in demand?

Databricks engineers are in high demand due to the growing adoption of cloud-based data analytics and machine learning platforms. They typically require skills in Spark, SQL, and cloud environments like AWS or Azure, making them valuable in data-driven organizations across various industries.

What are popular job titles related to Databricks Engineer jobs in Cupertino, CA?

For Databricks Engineer jobs in Cupertino, CA, the most frequently searched job titles are:

What job categories do people searching Databricks Engineer jobs in Cupertino, CA look for?

The top searched job categories for Databricks Engineer jobs in Cupertino, CA are:

What cities near Cupertino, CA are hiring for Databricks Engineer jobs?

Cities near Cupertino, CA with the most Databricks Engineer job openings:

Databricks Engineer jobs near you
Infographic showing various Databricks Engineer job openings in Cupertino, CA as of August 2026, with employment types broken down into 87% Full Time, 9% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $137,725 per year, or $66.2 per hour.

Databricks Engineer

iLink Digital

Milpitas, CA • On-site

Full-time

Re-posted 3 days ago

Job description


Role: Databricks Engineer
Experience
4-8 years
Location
As per business requirement
Employment Type
Full-time
Job Summary
We are looking for a skilled Databricks Engineer with strong expertise in designing, developing, and optimizing modern data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have experience building scalable ETL/ELT pipelines, working with large-scale data, and leveraging Apache Spark to deliver high-performance data solutions.
Key Responsibilities
  • Design, develop, and maintain scalable data pipelines using Databricks.
  • Build ETL/ELT workflows for batch and streaming data processing.
  • Develop solutions using PySpark, Spark SQL, and Delta Lake.
  • Implement Medallion Architecture (Bronze, Silver, Gold) for data transformation.
  • Integrate data from various sources including relational databases, APIs, cloud storage, and streaming platforms.
  • Optimize Spark jobs for performance, scalability, and cost efficiency.
  • Collaborate with Data Architects, Data Scientists, BI developers, and business stakeholders.
  • Implement CI/CD pipelines and deployment automation for Databricks workloads.
  • Ensure data quality, security, governance, and compliance.
  • Monitor, troubleshoot, and optimize production data pipelines.
  • Document technical solutions and follow engineering best practices.
Required Skills
Core Technologies
  • Databricks Lakehouse Platform
  • Apache Spark
  • PySpark
  • Spark SQL
  • Delta Lake
  • Python
  • SQL
Cloud Platforms (one or more)
  • Microsoft Azure (preferred)
  • AWS
  • Google Cloud Platform
Azure Technologies (Preferred)
  • Azure Data Factory (ADF)
  • Azure Data Lake Storage (ADLS Gen2)
  • Azure Synapse Analytics
  • Azure Key Vault
  • Azure DevOps
Data Engineering
  • Data Warehousing
  • Data Modeling
  • ETL/ELT Development
  • Batch Processing
  • Streaming (Kafka/Event Hubs)
  • Data Lake Architecture
DevOps & Version Control
  • Git
  • Azure DevOps / GitHub
  • CI/CD Pipelines
Preferred Qualifications
  • Experience with Unity Catalog.
  • Knowledge of Databricks Workflows and Jobs.
  • Hands-on experience with Delta Live Tables (DLT).
  • Exposure to MLflow is an added advantage.
  • Experience with data governance and security best practices.
  • Familiarity with Infrastructure as Code (Terraform) is a plus.
Educational Qualification
  • Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
Preferred Certifications
  • Databricks Certified Data Engineer Associate
  • Databricks Certified Data Engineer Professional
  • Microsoft Certified: Azure Data Engineer Associate (DP-203)
  • Azure Fundamentals (AZ-900)
Good to Have
  • Experience with real-time analytics.
  • Knowledge of Lakehouse architecture.
  • Experience with Agile/Scrum methodologies.
  • Strong analytical and problem-solving skills.
  • Excellent communication and stakeholder management abilities.

Mandatory Skills
  • Databricks
  • PySpark
  • Spark SQL
  • Delta Lake
  • Python
  • SQL
  • Azure/AWS/GCP (at least one cloud platform)
  • ETL/ELT Development
  • Data Lake Architecture
Nice to Have
  • Unity Catalog
  • Delta Live Tables (DLT)
  • MLflow
  • Kafka/Event Hubs
  • Azure Data Factory
  • Terraform
  • Azure DevOps/GitHub Actions

Apply