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Big Data Software Engineer Jobs in Texas (NOW HIRING)

Wise Skulls Corp.

Full Time Role: Big Data Engineer

Dallas, TX · On-site

$55.50 - $73.25/hr

We are looking for an experienced Big Data Engineer with 7-10 years of hands-on experience to design, build, and maintain scalable data pipelines and processing systems in an on-premises Big Data ...

K-Tek Resourcing LLC

Java/Scala Big Data Engineer

Austin, TX · On-site

$55.25 - $73/hr

Senior Java/Scala Big Data Engineer Location: Sunnyvale, CA or Austin, TX Candidate Type: Hands-on technical coder Core requirements * Strong hands-on Java and/or Scala development * Apache Spark ...

Cymertek

Big Data Architect

San Antonio, TX · On-site

$58 - $74.50/hr

Big Data Architect LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note this ... Software Engineering, Mathematics, Electrical Engineering, Statistics, Business Analytics ...

Ruri Software Technologies LLC

Data Engineer

Irving, TX · On-site

$109K - $132K/yr

Experience or knowledge of big data tools: i.e., Hadoop, Spark, Kafka. * Experience or knowledge of software engineering tools/practices: i.e., Github,VSCode, CI/CD * Hands-on experience in designing ...

Socket.dev

Senior Data Software Engineer

Houston, TX · On-site

$140 - $210/hr

As a Senior Data Software Engineer at JPMorganChase within the Commercial and Investment Bank, you are an integral part of an agile team that works to enhance, build, and deliver trusted market ...

New

Dexian DISYS

Big Data Engineer - Sr

Plano, TX · On-site

$53.50 - $71/hr

Data Engineer Location: Plano TX (3 Days/ Hybrid) Duration: 7 Months + Interview: 1 Round Role Summary * Large-scale enterprise migration initiative involving migration of existing data pipelines to ...

ZipRecruiter

Sr Software Engineer

Austin, TX

$121K - $160K/yr

We are looking for a dynamic and passionate Sr. Software Engineer in the Austin area with expertise in cloud technologies, big data and distributed systems. To apply, please email your resume to hr ...

Sarian, Inc.

Sr. Pyspark Data Engineer

Irving, TX · On-site

$109K - $132K/yr

You will work closely with data analysts, data scientists, and software engineers to design, develop, and optimize scalable data processing solutions using PySpark, Apache Spark, and other big data ...

Foxconn Assembly LLC

Data Engineer

Houston, TX · On-site

$109K - $131K/yr

This role will manage big data that are cross-factory, cross-business-units and cross-systems ... Any software programming certifications are a plus.

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All JobsBig Data Software Engineer JobsBig Data Software Engineer Jobs in Texas

Big Data Software Engineer information

See Texas salary details

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$82

How much do big data software engineer jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for big data software engineer in Texas is $58.68, according to ZipRecruiter salary data. Most workers in this role earn between $49.95 and $66.06 per hour, depending on experience, location, and employer.

What is a big data software engineer?

A Big Data Software Engineer is a technology professional who designs, develops, and maintains systems and applications that process large and complex data sets. They work with big data technologies like Hadoop, Spark, and NoSQL databases to enable efficient data storage, processing, and analysis. These engineers collaborate with data scientists, analysts, and other stakeholders to build scalable solutions that extract valuable insights from massive amounts of data. Their work is essential in industries such as finance, healthcare, e-commerce, and more, where data-driven decision-making is critical.

What does a big data software engineer do?

As a big data software engineer, your primary responsibilities are to collect, store, process, and analyze large amounts of data. You then build what the software architects design. Your duties are to develop software and choose solutions to maintain, implement, and continue monitoring the data. You also need to integrate your code with the current company architecture. You may handle high scale distributed systems, build and interact with algorithms, and work in cloud computing environments.

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

To thrive as a Big Data Software Engineer, you need strong programming skills (often in Java, Scala, or Python), a solid understanding of distributed systems, and a degree in computer science or related field. Experience with big data tools like Hadoop, Spark, Kafka, as well as familiarity with cloud platforms and data pipeline frameworks, is typically required. Problem-solving, analytical thinking, and effective teamwork are essential soft skills for handling complex data challenges and collaborating across teams. These skills and qualifications are crucial for designing efficient, scalable data solutions that drive business insights and innovation.

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

AspectBig Data Software EngineerData Engineer
Primary FocusDeveloping and optimizing big data processing applicationsBuilding and maintaining data pipelines and infrastructure
Skills & CertificationsProgramming (Java, Scala), Hadoop, Spark, SQLETL tools, SQL, cloud platforms, scripting
Work EnvironmentData processing teams, software development projectsData infrastructure teams, cloud environments
Industry UsageTech, finance, healthcare, retailTech, finance, healthcare, retail

While both roles work with big data technologies, Big Data Software Engineers focus on creating scalable data processing applications, whereas Data Engineers build and maintain the data pipelines and infrastructure that support these applications. Both roles often collaborate but have distinct technical responsibilities.

What are the most common challenges big data software engineers face when working with large-scale data systems?

Big Data Software Engineers often encounter challenges such as ensuring data quality, optimizing the performance of data pipelines, and managing the complexity of distributed systems. Handling massive volumes of data requires careful attention to system scalability and fault tolerance. Additionally, integrating new technologies and collaborating with data scientists, analysts, and DevOps teams to deploy and maintain reliable data solutions are key aspects of the role. Staying updated on evolving big data frameworks and best practices is crucial for continued success.
More about Big Data Software Engineer jobs
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What job categories do people searching Big Data Software Engineer jobs in Texas look for? The top searched job categories for Big Data Software Engineer jobs in Texas are:
What are popular job titles related to Big Data Software Engineer jobs in TX? For Big Data Software Engineer jobs in TX, the most frequently searched job titles are:
Big Data Software Engineer jobs near you
Infographic showing various Big Data Software Engineer job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $122,047 per year, or $58.7 per hour.

Full Time Role: Big Data Engineer

Wise Skulls Corp.

Dallas, TX • On-site

$55.50 - $73.25/hr

Other

Posted 5 days ago

Job description

Summary: It is a Full Time Role.

We are looking for an experienced Big Data Engineer with 7–10 years of hands-on experience to design, build, and maintain scalable data pipelines and processing systems in an on-premises Big Data environment. Beyond strong individual contribution, the ideal candidate will own architecture and design decisions, set technical direction, and mentor and support other developers on the team. The role works closely with cross-functional teams to deliver reliable, high-quality data solutions that support business and analytics needs.

Technical Skills

Must Have

  • 7–10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development teams).
  • Python – strong hands-on development experience building production-grade data solutions.
  • Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) – deep, hands-on experience in on-premises environments.
  • Apache Spark – solid experience developing and tuning large-scale distributed data processing jobs.
  • Job scheduling / orchestration – hands-on experience with CA7 or Control-M (or comparable enterprise schedulers).
  • Strong understanding of data structures, ETL processes, and SQL.
  • Extensive experience with large-scale data processing and distributed systems.
  • Demonstrated ability to make sound architecture/design decisions and to mentor and support other developers.
  • Exposure to AI/ML concepts or tools, with a strong willingness to learn and grow in this space.
  • Banking/Financial domain highly preferred

Roles & Responsibilities

  • Lead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.
  • Own architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.
  • Mentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.
  • Build and optimize data workflows using Python, Spark, and the Hadoop ecosystem.
  • Work extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.
  • Manage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.
  • Ensure data quality, integrity, and performance across data platforms.
  • Collaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.
  • Troubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.
  • Champion best practices for coding standards, version control, testing, and documentation.
  • Stay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.
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