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Data Software Engineer Jobs (NOW HIRING)

Senior Big Data Software Engineer

Cupertino, CA · On-site

$68.75 - $91/hr

... Engineer, Software Developer, and/or Programmer role using Java with a focus on backend/database, and Big Data solutions * Demonstrated skill across Spring, Hibernate, Eclipse, Lucene, Solr, SOAP ...

Data and Software Engineer

Mclean, VA · On-site

$115K - $139K/yr

Data and Software Engineer McLean, VA $200K to $250K TS/SCI and FSP The Data & Software Engineer works with a small team to build complex data flows for a custom application. Successful candidate ...

$175K - $250K/yr

Software Engineer - C++ Core Data Build and optimize real-time on-vehicle data capture and storage infrastructure Location: Foster City, California Compensation: $175,000 - 250,000 USD / year About ...

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DATA Software Engineer information

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$44.5K

$129.7K

$177.5K

How much do data software engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for data software engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is a data software engineer?

Data Software Engineers are professionals who design, build, and maintain the software systems that enable organizations to collect, process, and analyze large volumes of data. They bridge the gap between data engineering and software development by creating scalable, efficient pipelines and applications that support data-driven decision making. Their responsibilities often include developing data processing frameworks, ensuring data quality, and collaborating with data scientists and analysts to deliver actionable insights.

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

To thrive as a Data Software Engineer, you need strong programming skills (often in Python, Java, or Scala), a solid understanding of data structures and algorithms, and a background in computer science or a related field. Familiarity with big data frameworks (like Hadoop or Spark), database systems (SQL/NoSQL), and data pipeline tools is typically required, along with relevant certifications such as AWS Certified Data Analytics. Excellent problem-solving abilities, collaboration, and effective communication are soft skills that set top performers apart. These skills ensure the efficient design, development, and optimization of robust data systems critical for driving business insights and decision-making.

What are some common challenges data software engineers face when working with large datasets?

Data Software Engineers often encounter challenges related to scalability, data quality, and system performance when handling large datasets. Ensuring that data pipelines can efficiently process high volumes of data without bottlenecks requires robust architecture and frequent optimization. Additionally, maintaining data integrity and consistency across distributed systems can be complex, especially when integrating data from multiple sources. Collaboration with data scientists, analysts, and DevOps teams is key to overcoming these challenges and building reliable, efficient data solutions.

What is the difference between Data Software Engineer vs Data Engineer?

AspectData Software EngineerData Engineer
Primary FocusDeveloping software tools and applications for data processing and analysisBuilding and maintaining data pipelines and infrastructure
Skills & CertificationsProgramming, software development, data modeling, often with certifications in software engineeringDatabase systems, ETL tools, cloud platforms, often with certifications in data engineering
Work EnvironmentSoftware development teams, data science teams, often in tech companiesData infrastructure teams, IT departments, cloud service providers

While both roles work with data, Data Software Engineers focus on creating software solutions for data analysis, whereas Data Engineers build the infrastructure to collect, store, and process data efficiently. Both roles require programming skills and often overlap, but their core responsibilities differ in scope and focus.

More about DATA Software Engineer jobs

What cities are hiring for Data Software Engineer jobs?

Cities with the most Data Software Engineer job openings:

What are the most commonly searched types of Data Software Engineer jobs?

The most popular types of Data Software Engineer jobs are:

Who are the top companies hiring for Data Software Engineer jobs?

The top employers for Data Software Engineer jobs are:

What states have the most Data Software Engineer jobs?

States with the most job openings for Data Software Engineer jobs include:

What are popular job titles related to Data Software Engineer jobs?

For Data Software Engineer jobs, the most frequently searched job titles are:

Infographic showing various Data Software Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Software Engineer III - ML Ops

Milwaukee, WI • On-site

Northwestern Mutual Life Insurance Company
Finance and Insurance • 5 - 10K employees

$112K - $135K/yr

Full-time

Posted 12 days ago


Key responsibilities

  • Build and standardize services, data pipelines, automation, and dashboards for the ML Ops platform.

  • Develop reliable data pipelines to transform and aggregate data from source systems and data platforms.

  • Collaborate with data scientists, software engineers, and infrastructure teams to enable automation, monitoring, and deployment of machine learning models.


Northwestern Mutual rating

8.0

Company rating: 8.0 out of 10

Based on 76 frontline employees who took The Breakroom Quiz


Job description

This is a hybrid position. 3 days onsite at our downtown Milwaukee Corporate Office.
Northwestern Mutual (NM) has been helping people and businesses achieve financial security for over 169 years. Through a distinctive, whole-picture planning approach including both insurance and investments, we empower people to be financially confident. We combine the expertise of our financial professionals with a personalized digital experience and groundbreaking technology to best serve our clients.
About the Job
Data is a critical driver of this approach and a cornerstone for how we engage with our customers. To help lead the effort, NM's Assistant Director, Data Software Engineering - AI/ML Ops is seeking a highly motivated, curious, and passionate software engineers to build and design services, data pipelines, automation, and dashboards for our ML Ops platform and to implement and standardize practices for traditional and generative artificial intelligence.
You will be joining our Data Solutions and Enablement department (DSE) whose mission is to unlock and provide analytical insight on our core customer and client data to better serve our customers, field representative, and business partners. As a part of the team you will collaborate with Data Scientists, Software Engineers, Data Engineers, and Product Owners throughout the organization to help unlock the value of data through predictive analytics, operationalized machine learning, applied AI and generative AI.
What You'll do
ML Ops Team responsibilities include but are not limited to:
  • Building and standardizing services and patterns in Python and Java to enable model deployment, training, inference, and monitoring.
  • Building services and automation to streamline and manage the stages of the AI/ML life cycle and model governance
  • Develop reliable data pipelines that transform and aggregate data from NM's source systems and data platforms
  • Establish and maintain NM's data science, ML and AI platforms, with a focus on rapid iteration and operational deployment of predictive models, and cost management of workloads
  • Integrating various ML Ops platforms together such as Databricks, AWS Sagemaker, AWS Bedrock.
  • Establish a feature store of curated metrics, attributes, and features for ML models
  • Collaborate closely with data scientists, DevOps Engineers, and enterprise infrastructure teams to enable automation and monitoring across the machine learning lifecycle
  • Develop ML model monitoring pipelines for model performance, data quality, and gen AI evaluation, tracing, and metrics.

AI/ML Ops Baseline Competencies:
  • We work in Python and Java, leveraging Spring, Flask, FastMCP, FastMCP, Pandas, Spark, LangGraph and ML Flow
  • We leverage AWS and Databricks often and deploy software and AI/ML solutions CI/CD first. We aspire to automate and standardize everything.
  • We expect proficiency with databases and SQL from RDBMS (Postgres, SQL Server, MySql etc.) or big data platforms (Databricks, Spark, Redshift, Snowflake, Big Query etc).
  • We expect familiarity and experience with basic ML algorithms, LLMs, and GenAI/Agentic concepts.
  • We expect an understanding of basic tools and libraries common to data science, AI and ML. e.g. ML Flow, Pandas/Numpy/Sklearn, PyTorch/TensorFlow, or LlamaIndex/LangChain/LangGraph OR a strong mathematical and computer science background.
  • We are passionate about continuous learning and problem solving. Curiosity is expected, welcome, and rewarded.
  • We collaborate and work creatively every day.

The Data Software Engineer III leads the design and implementation of complex data systems, leveraging advanced data engineering techniques and emerging leadership skills.
Primary Duties & Responsibilities
  • Architect and develop scalable data pipelines using advanced programming skills
  • Gather and translate data requirements into technical solutions
  • Optimize sophisticated data integration and transformation processes
  • Enhance existing systems for performance and scalability
  • Mentor junior engineers and oversee CI/CD pipelines

What You'll Bring to the Role
  • Bachelor's degree in Computer Science, Engineering, or equivalent experience
  • Strong expertise in programming languages for data engineering
  • Experience with data processing frameworks and Kubernetes
  • Proficiency with cloud platforms (e.g., AWS, Azure, Google Cloud) and data visualization tools
  • Understanding of machine learning concepts
  • Expertise in CI/CD processes and version control
  • Expertise in source code management using Git and GitFlow
  • Strong understanding of CI/CD processes and tools (e.g., Jenkins, GitLab CI/CD, CircleCI) and experience with artifact repositories (e.g., Nexus, Artifactory)
  • Strong understanding of agile methodologies and experience in an agile development environment

Skills You Have
Adaptive Communication (NM) - Formulates strategies to be used to convey complex information about services, products, systems, or processes to targeted audiences; communicates and liaises between technical and non-technical audiences.
Analytical Thinking (NM) - Organizes and compares various aspects of a situation to comprehend and identify key or underlying complex issues through the use of quantitative data and analysis; leverages strong business acumen, problem solving, and interpersonal skills to think critically about situations from multiple perspectives and consistently seeks ways to improve processes.
Consulting (NM) - Connects with stakeholders to understand and gain specific information to help resolve customer problems in a given domain. Communicates effectively intent to customers, solicits customer requirements, utilizes domain knowledge and collaborates with the right stakeholders.
Databases & Data Platforms (NM) - Utilizes knowledge of databases to access, manage, and update information, typically containing aggregations of data records or files; includes understanding of different types of databases.
Engineering Expertise & Practices (NM) - Applies specialized experiences in different facets of engineering, including data, applications, cyber, systems, operations, product, security, and testing, along with technical aptitude to adapt new expertise as they become relevant through an understanding of underlying engineering principles.
Machine Learning (NM) - Applies understanding of and/or computes large data structures and sets using quantitative analysis methods, while building out data pipelines and statistics.
Programming Languages (NM) - Demonstrates proficiency in one or more programming languages to execute activities, tasks, practices, and deliverables associated with writing and modifying programs and scripts that comprise an application system; designs, codes, tests, and installs complex computer programs and maintains detailed documentation of programming tasks.
#LI-Hybrid
Compensation Range:
Pay Range - Start:
$108,160.00
Pay Range - End:
$162,240.00
Geographic Specific Pay Structure:
Structure 110:
Structure 115:
We believe in fairness and transparency. It's why we share the salary range for most of our roles. However, final salaries are based on a number of factors, including the skills and experience of the candidate; the current market; location of the candidate; and other factors uncovered in the hiring process. The standard pay structure is listed but if you're living in California, New York City or other eligible location, geographic specific pay structures, compensation and benefits could be applicable, click here to learn more.
Grow your career with a best-in-class company that puts our clients' interests at the center of all we do. Get started now!
Northwestern Mutual is an equal opportunity employer that welcomes talented individuals of all backgrounds. We are committed to creating and maintaining an environment in which each employee can contribute creative ideas, seek challenges, assume leadership and continue to focus on meeting and exceeding business and personal objectives.

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About Northwestern Mutual

Sourced by ZipRecruiter

Northwestern Mutual has been helping families and businesses achieve financial security for over 160 years through a distinctive planning approach that integrates risk management with wealth accumulation, preservation, and distribution. With more than $290 billion in assets, $30 billion in revenues and more than $1.9 trillion worth of life insurance protection in force, Northwestern Mutual delivers financial security to more than 4.6 million clients. People are the power behind Northwestern Mutual, and diversity makes us better. We are committed to reflecting and serving the marketplace. We do so by attracting and improving the engagement of those who bring their outstanding perspectives, ideas, and beliefs.

Industry

Finance and insurance

Company size

5,001 - 10,000 Employees

Headquarters location

Milwaukee, WI, US