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Databricks Software Jobs in La Porte, IN (NOW HIRING)

CFS

Data Engineer

South Bend, IN · Hybrid

$150K/yr

Hands-on experience with Databricks * Strong proficiency in Python and Spark * Experience developing and optimizing ETL/ELT pipelines * Background working with legacy Microsoft tools such as SSIS and ...

All JobsDatabricks Software JobsDatabricks Software Jobs in La Porte, IN

Databricks Software information

See La Porte, IN salary details

$44.4K

$103.5K

$153.7K

How much do databricks software jobs pay per year?

As of Jul 26, 2026, the average yearly pay for databricks software in La Porte, IN is $103,547.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,300.00 and $120,400.00 per year, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior software engineers, especially those working in high-demand fields like data engineering or cloud engineering at large tech companies, can earn $500,000 or more annually. These roles often require extensive experience, advanced skills in programming and cloud platforms, and may include bonuses or stock options that contribute to total compensation.

What is Databricks Software?

Databricks Software is a unified analytics platform built on Apache Spark that provides tools for big data processing, machine learning, and collaborative data science. It enables organizations to store, manage, and analyze large datasets efficiently, supporting both batch and streaming data workloads. Databricks also offers collaborative notebooks, automated workflows, and integrations with cloud storage and data lakes, making it a popular choice for data engineering, data science, and business analytics teams.

How much do Databricks employees make?

Salaries for Databricks software roles vary based on experience, location, and specific position, but the average annual salary for software engineers at Databricks typically ranges from $100,000 to $150,000. Senior roles and specialized skills in data engineering or cloud platforms can command higher compensation. Benefits often include stock options, bonuses, and professional development opportunities.

Is Databricks a high paying job?

Working as a Databricks software engineer or data scientist typically offers above-average salaries compared to other tech roles, reflecting the specialized skills in cloud platforms, big data, and Spark. Compensation varies based on experience, location, and certifications, but generally includes competitive base pay, bonuses, and stock options. These roles often require knowledge of programming languages like Python or Scala and familiarity with cloud environments such as AWS or Azure.

What are some common challenges faced by Databricks Software Engineers, and how can they be overcome?

Databricks Software Engineers often encounter challenges related to scaling big data pipelines, optimizing Spark workloads, and integrating diverse data sources. Navigating the complexity of distributed systems and managing cloud infrastructure can be demanding, especially when ensuring data reliability and security. To overcome these challenges, engineers typically collaborate closely with data scientists, DevOps, and platform teams, leverage Databricks' extensive documentation and community support, and adopt best practices such as version control and continuous integration. Regular knowledge sharing and staying updated with new features also help engineers succeed in this dynamic environment.

What are the key skills and qualifications needed to thrive as a Databricks Software Engineer, and why are they important?

To thrive as a Databricks Software Engineer, you need strong programming skills in languages like Python, Scala, or Java, as well as a solid understanding of distributed computing and data engineering concepts. Familiarity with Databricks platform, Apache Spark, cloud services (such as AWS or Azure), and relevant certifications like Databricks Certified Data Engineer are highly valued. Excellent problem-solving abilities, collaboration, and effective communication are important soft skills for this role. These skills ensure efficient development, deployment, and optimization of big data solutions that drive business insights and innovation.

What exactly are Databricks Jobs?

Databricks Jobs are automated tasks or workflows that run on the Databricks platform, typically involving data processing, machine learning, or analytics tasks. They can be scheduled, monitored, and managed through the Databricks workspace, requiring knowledge of Spark, SQL, or Python scripting. Job roles often involve configuring clusters and ensuring efficient execution of data pipelines.

What is the difference between Databricks Software vs Data Engineer?

AspectDatabricks SoftwareData Engineer
Primary RolePlatform for data analytics and machine learningBuilds, maintains data pipelines and infrastructure
Required SkillsSQL, Spark, cloud platforms, data science basicsSQL, ETL, programming (Python, Scala), database management
Work EnvironmentCloud-based, collaborative data platformData teams, cloud or on-premises environments
CertificationsDatabricks certifications, cloud certificationsNone specific, often cloud or data certifications

While Databricks Software provides a platform for data analytics and machine learning, Data Engineers focus on building and maintaining data pipelines and infrastructure. Both roles often work together but have distinct responsibilities and skill sets within the data ecosystem.

Databricks Software jobs near you
Data Engineer

Data Engineer

CFS

South Bend, IN • Hybrid

$150K/yr

Full-time

Posted 15 days ago

Job description

Data Engineer
Location: South Bend, IN (Hybrid)
Salary: Up to $125,000 (Mid-Level) | Up to $150,000 (Senior-Level)

About the Role

An organization in South Bend, IN is seeking a Data Engineer to play a critical role in building and optimizing a centralized enterprise data platform. This position is ideal for a hands-on engineer who thrives in modern Azure environments and has strong experience designing scalable data pipelines using Databricks, Python, and Spark.

As the organization consolidates data into a single, unified platform, this role will focus on engineering robust ETL/ELT processes, modernizing legacy data workflows, and ensuring reliable, high-performance data delivery across the enterprise.

This is a highly visible role working closely with analytics, IT, and business stakeholders to support enterprise-wide reporting, operational intelligence, and long-term data strategy.

What We’re Looking For Must-Have Skills

  • Strong experience with Azure data services (Azure Data Factory, Azure SQL, Synapse, etc.)

  • Hands-on experience with Databricks

  • Strong proficiency in Python and Spark

  • Experience developing and optimizing ETL/ELT pipelines

  • Background working with legacy Microsoft tools such as SSIS and SSRS

  • Deep understanding of data modeling, transformation logic, and pipeline architecture

  • Experience consolidating data into centralized or cloud-based platforms

  • Strong SQL skills

  • Ability to troubleshoot performance issues and optimize large-scale datasets

  • Strong communication skills and ability to collaborate cross-functionally

Nice-to-Have

  • Experience supporting enterprise BI/reporting environments

  • Exposure to ERP, operational, or transactional data systems

  • Experience in manufacturing, agriculture, supply chain, or similar industries

  • Familiarity with data governance and best practices

Key Responsibilities Data Engineering & Platform Development

  • Design, build, and maintain scalable data pipelines using Databricks, Azure, Python, and Spark

  • Migrate and modernize legacy SSIS/SSRS workflows into the centralized data platform

  • Develop robust ETL/ELT processes to integrate data from multiple enterprise systems

Data Architecture & Optimization

  • Support the design of a unified enterprise data model

  • Optimize data processing performance and reliability

  • Ensure data quality, validation, and integrity across pipelines

Collaboration & Strategy

  • Partner with BI, analytics, and business teams to support enterprise reporting needs

  • Contribute to data standards, governance, and architectural best practices

  • Support the organization’s transition to a modern, centralized data ecosystem


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