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Java Data Engineer Jobs in New Baltimore, MI (NOW HIRING)

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Comprehensive experience with one or more programming languages such as Python, Java, or Rust * Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3 ...

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Stellantis is looking for a Senior Data Engineer to join their AI & Data Analytics Team. In this ... Strong proficiency in Scala or Java. You should be comfortable building production-grade ...

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Comprehensive experience with one or more programming languages such as Python, Java, or Rust * Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3 ...

OptechUs

Java Developer

Auburn Hills, MI

$48.25 - $62.50/hr

Java Developer - Analyzing and interpreting functional requirements into design, development and ... data storage & access techniques including abstraction layers, DAO's and RDBMS such as SQL Server ...

Integrated Resources INC

Java Developer

Detroit, MI · On-site

$50.50 - $65.25/hr

Java Developer Full Time permanent position Location: Detroit, MI The Java Developer will perform ... data persistence layer framework design and development. Experience with Hibernate and Spring ...

Nastech Global

Developer

Detroit, MI · On-site

... Java, or Shell scripting for data operations. - Familiarity with DevOps practices, tools (e.g., Jenkins, Docker, Kubernetes), and infrastructure automation (e.g., Terraform). - Knowledge of data ...

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Java Data Engineer information

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How much do java data engineer jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for java data engineer in New Baltimore, MI is $54.90, according to ZipRecruiter salary data. Most workers in this role earn between $45.19 and $62.02 per hour, depending on experience, location, and employer.

What is a Java data engineer?

A Java Data Engineer is a technology professional who designs, develops, and maintains data processing systems using Java programming language. They work with large datasets, build data pipelines, and ensure the efficient movement, transformation, and storage of data. Java Data Engineers often collaborate with data scientists, analysts, and other engineers to support data-driven decision-making in organizations. Their expertise typically includes Java, SQL, big data technologies like Hadoop or Spark, and cloud platforms. They play a crucial role in enabling reliable and scalable data infrastructure for businesses.

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

To thrive as a Java Data Engineer, you need strong programming skills in Java, a solid understanding of data structures, SQL, and experience with big data frameworks, often supported by a degree in computer science or a related field. Familiarity with data processing tools like Apache Spark, Hadoop, Kafka, and experience with cloud data platforms (e.g., AWS, GCP) or relevant certifications are typically required. Analytical thinking, problem-solving ability, and effective communication are crucial soft skills for collaborating with teams and interpreting data requirements. These capabilities are essential for building reliable, scalable data solutions that support business intelligence and analytics needs.

What are the most common challenges faced by Java data engineers when working with large-scale data pipelines?

Java Data Engineers often encounter challenges with optimizing the performance and scalability of data pipelines, especially as data volumes grow. They must ensure data integrity and consistency while managing distributed systems and integrating with various data sources. Debugging issues in real-time data processing and maintaining efficient, fault-tolerant code are also key hurdles. Collaborating closely with data scientists, database administrators, and DevOps teams is essential to overcome these challenges and deliver reliable data solutions.

What is the difference between Java Data Engineer vs Python Data Engineer?

AspectJava Data EngineerPython Data Engineer
Required CredentialsBachelor's in Computer Science, Java certificationsBachelor's in Computer Science, Python certifications
Work EnvironmentBig data platforms, Java-based toolsData analysis, scripting, Python-based tools
Employer & Industry UsageFinancial services, enterprise systemsTech startups, data science projects
Common Search & ComparisonYesYes

Java Data Engineers and Python Data Engineers often share similar roles in data processing and engineering. The main difference lies in the programming languages used: Java is common in large-scale enterprise environments, while Python is favored for data analysis and scripting. Both roles require strong programming skills, but their toolsets and typical applications differ based on industry needs.

What cities near New Baltimore, MI are hiring for Java Data Engineer jobs?

Cities near New Baltimore, MI with the most Java Data Engineer job openings:

Java Data Engineer jobs near you

Data Engineer

Stellantis

Auburn Hills, MI • On-site

$108K - $130K/yr

Full-time

Re-posted 19 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

15th of 45 rated automakers

Job description

The AI & Data Analytics Team is looking for a Senior Data Engineer to join our team. In this role, you will be responsible for designing, building, and optimizing robust data pipelines that process massive datasets in both batch and real-time. You will work at the intersection of software engineering and data science, ensuring that our data architecture is scalable, reliable, and follows industry best practices. 

Priorities can change in a fast-paced environment like ours, so this role includes, but is not limited to, the following responsibilities: 

  • Pipeline Development: Design and implement complex data processing pipelines using Apache Spark. 
  • Architectural Leadership: Build scalable, distributed systems that handle high-throughput data streams and large-scale batch processing. 
  • Infrastructure as Code: Manage and provision cloud infrastructure using Terraform. 
  • CI/CD & Automation: Streamline development workflows by implementing and maintaining GitHub Actions for automated testing and deployment. 
  • Code Quality: Uphold rigorous software engineering standards, including comprehensive unit/integration testing, code reviews, and maintainable documentation. 
  • Collaboration: Work closely with stakeholders to translate business requirements into technical specifications. 

Basic Qualifications: 

  • Bachelors degree in Computer Science, Engineering, Mathematics, or a related technical discipline  
  • A minimum of 5 years of experience in the data engineering and software development life cycle. Including:
    • A minimum of 4 years of hands-on experience in building and maintaining production data applications, current experience in both relational and columnar data stores. 
    • A minimum of 4 years of hands-on experience working with AWS cloud services  
  • Comprehensive experience with one or more programming languages such as Python, Java, or Rust 
  • Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3, etc.) 
  • Familiarity with time series database, data streaming applications, event driven architectures, Kafka, Flink, and more 
  • Experience with workflow management engines (i.e., Airflow, Luigi, Azure Data Factory, etc.) 
  • Experience with designing and implementing real-time pipelines 
  • Experience with data quality and validation 
  • Experience with API design 
  • Distributed Computing: Deep expertise in Apache Spark (Core, SQL, and Structured Streaming). 
  • Programming Mastery: Strong proficiency in Scala or Java. You should be comfortable building production-grade applications in a JVM-based environment. 
  • SQL Proficiency: Advanced knowledge of SQL for data transformation, analysis, and performance tuning.

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