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Databricks Engineer Jobs in Schaumburg, IL (NOW HIRING)

1 point system

Databricks Engineer

Chicago, IL · On-site

$118K - $141K/yr

Key Responsibilities Data Engineering & Pipeline DevelopmentDesign, develop, and maintain end-to-end data pipelines in Databricks using Spark and Delta Lake Build and optimize ELT/ETL processes for ...

Resultant

Data Engineer - Databricks

Chicago, IL · On-site +1

$118K - $141K/yr

We're looking for a Data Engineer to join our Databricks practice and help design, build, and optimize Lakehouse-based data platforms for clients across industries - from public sector agencies to ...

New

Resultant

Data Engineer - Databricks

Chicago, IL

$118K - $141K/yr

We're looking for a Data Engineer to join our Databricks practice and help design, build, and optimize Lakehouse-based data platforms for clients across industries - from public sector agencies to ...

New

Epsilon

Lead Databricks Platform Engineer

Chicago, IL · On-site

$105K - $139K/yr

Lead and scale Databricks platform administration across enterprise workspaces and multi-cloud ... Partner cross-functionally with Engineering, Security, Cloud, and Operations teams to ensure secure ...

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All JobsDatabricks Engineer JobsDatabricks Engineer Jobs in Schaumburg, IL

Databricks Engineer information

See Schaumburg, IL salary details

$58.4K

$109.6K

$199.3K

How much do databricks engineer jobs pay per year?

As of Aug 13, 2026, the average yearly pay for databricks engineer in Schaumburg, IL is $109,618.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,000.00 and $130,100.00 per year, depending on experience, location, and employer.

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 $100,000 to $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized skills in Spark, cloud platforms, or data architecture can earn higher compensation, often exceeding $160,000 per year.

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.

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, and their roles are often available across various industries seeking data-driven solutions.

What are popular job titles related to Databricks Engineer jobs in Schaumburg, IL?

For Databricks Engineer jobs in Schaumburg, IL, the most frequently searched job titles are:

What job categories do people searching Databricks Engineer jobs in Schaumburg, IL look for?

The top searched job categories for Databricks Engineer jobs in Schaumburg, IL are:

What cities near Schaumburg, IL are hiring for Databricks Engineer jobs?

Cities near Schaumburg, IL with the most Databricks Engineer job openings:

Databricks Engineer jobs near you
Infographic showing various Databricks Engineer job openings in Schaumburg, IL as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $109,618 per year, or $52.7 per hour.

Databricks Engineer

1 point system

Chicago, IL • On-site

$118K - $141K/yr

Contractor

Re-posted 12 days ago

Job description

Job Description:

Key Responsibilities

Data Engineering & Pipeline DevelopmentDesign, develop, and maintain end-to-end data pipelines in Databricks using Spark and Delta Lake
Build and optimize ELT/ETL processes for structured and unstructured data ingestion into the Data Lakehouse
Implement scalable ingestion patterns (batch and event-driven) from internal systems, third-party APIs, and cloud sources
Develop data models (bronze, silver, gold layers) to support enterprise reporting, analytics, and downstream consumption
Data Platform & IntegrationIntegrate the Data Lakehouse with enterprise tools such as Tableau, Alteryx, and machine learning platforms
Design and implement data access controls, identity management, and secure data sharing mechanisms
Support API-based integrations and downstream data consumption patterns
Data Quality, Governance & ControlsImplement data quality checks, reconciliation processes, and monitoring within Databricks pipelines
Ensure adherence to enterprise data governance standards, including lineage, metadata, and audit requirements
Support regulatory and compliance requirements (e.g., data integrity, privacy, and security controls)
Cloud & AutomationDevelop and manage workflows using orchestration tools (e.g., Airflow, Control-M)
Automate data pipelines, deployments, and operational processes through CI/CD pipelines
Leverage cloud-native services (AWS/Azure) for data processing, storage, and event-driven architectures
Operations & SupportMonitor, troubleshoot, and optimize data pipelines and Spark workloads for performance and reliability
Support production data platforms, including incident resolution and root cause analysis
Ensure high availability, data integrity, and SLA adherence across enterprise data systems
CollaborationPartner with data architects, data scientists, BI teams, and business stakeholders to deliver data solutions
Participate in Agile ceremonies and contribute to iterative delivery of data products
Translate business requirements into scalable technical data solutions

Required Qualifications

8+ years of experience in data engineering, data platforms, or related roles
Hands-on experience with Databricks, Apache Spark (PySpark), and Delta Lake
Strong SQL and data modeling skills (relational and dimensional)
Experience building and supporting data pipelines in a cloud environment (AWS or Azure)
Experience with ELT/ETL tools (e.g., Fivetran, custom ingestion frameworks)
Familiarity with data orchestration tools (Airflow, Control-M)
Experience working in Agile development environments
Preferred QualificationsExperience in financial services or regulated environments (e.g., banking, risk, regulatory reporting)
Knowledge of data governance frameworks and tools (e.g., Collibra)
Experience with real-time or streaming data pipelines
Exposure to machine learning pipelines and feature engineering in Databricks
Cloud certifications (AWS, Azure, or Databricks)
Technical SkillsDatabricks (Lakehouse architecture, notebooks, jobs, Unity Catalog)
Spark / PySpark
SQL (advanced querying and optimization)