1

Jr Data Engineer Jobs in Prosper, TX (NOW HIRING)

Fresher JD Mandatory: · Solid understanding of data structures and algorithms (sort, select, search, trees, graphs, collections etc) · Academic hands-on experience in Java or Scala or Python code ...

Senior Engineer - Data

Richardson, TX · On-site

$112K - $215K/yr

Mentor + support engineers at all jr levels. Share best practices + improve processes within ... Three (3) years of rel exp in data software development, programming languages and developing with ...

Senior Engineer - Data

Richardson, TX · Hybrid

$112K - $215K/yr

Mentor + support engineers at all jr levels. Share best practices + improve processes within ... Three (3) years of rel exp in data software development, programming languages and developing with ...

Software Engineer Principal

Richardson, TX · On-site

$122K - $164K/yr

... Jr developers-guiding them through code reviews, knowledge-sharing workshops to accelerate their ... data privacy, hallucination mitigation, evaluation, and guardrails. • Solid hands-on experience ...

Jr QA Test Engineer

Plano, TX · On-site

$39.50 - $53.75/hr

Experience with data validation * Knowledge and experience of defect management * Team oriented, collaborative with ability to follow direction * Ability to adapt to ever changing environment

Jr Data Engineer information

See Prosper, TX salary details

$40.8K

$118.8K

$162.6K

How much do jr data engineer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for jr data engineer in Prosper, TX is $118,793.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,900.00 and $125,900.00 per year, depending on experience, location, and employer.

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

To thrive as a Jr Data Engineer, you need a solid understanding of database concepts, SQL, and programming languages like Python or Java, usually supported by a relevant degree or coursework. Familiarity with ETL tools, cloud platforms (such as AWS or Azure), and version control systems like Git is typically required. Strong problem-solving, attention to detail, and effective communication are important soft skills for collaborating with teams and handling data issues. These combined skills ensure efficient data pipeline development, accurate data handling, and smooth integration of data solutions within an organization.

What is the difference between Jr Data Engineer vs Data Analyst?

AspectJr Data EngineerData Analyst
Required SkillsBasic SQL, Python, ETL processes, data pipeline developmentData visualization, SQL, Excel, statistical analysis
Work EnvironmentData engineering teams, cloud platforms, data warehousesBusiness units, reporting tools, dashboards
CertificationsEntry-level data engineering certifications (e.g., Google Cloud, AWS)Data analysis certifications (e.g., Microsoft, Tableau)

The Jr Data Engineer typically focuses on building and maintaining data pipelines and infrastructure, requiring skills in SQL, Python, and ETL processes. In contrast, a Data Analyst concentrates on interpreting data, creating reports, and visualizations. Both roles often work within similar industries and environments but serve different functions in data management and analysis.

What does a Jr Data Engineer do?

A Jr Data Engineer is responsible for supporting the design, development, and maintenance of data pipelines and systems. They work with senior data engineers to collect, clean, and organize data from various sources, ensuring that the data is reliable and accessible for analysis. Junior data engineers may also assist in troubleshooting data issues, optimizing data flows, and learning best practices in data engineering. Their role provides foundational experience in data management, database technologies, and programming.

What types of projects and tasks can a Jr Data Engineer expect to work on during their first year?

As a Jr Data Engineer, you can expect to be involved in projects such as building and maintaining data pipelines, cleaning and transforming raw data, and supporting the integration of new data sources. You'll likely collaborate closely with data analysts, senior engineers, and business stakeholders to ensure data is accessible and reliable for analysis. During your first year, you'll gain experience with ETL tools, cloud data platforms, and scripting languages, while learning best practices in data architecture and quality assurance. This hands-on involvement lays a strong foundation for future growth into more advanced engineering or analytics roles.
What are popular job titles related to Jr Data Engineer jobs in Prosper, TX? For Jr Data Engineer jobs in Prosper, TX, the most frequently searched job titles are:
What job categories do people searching Jr Data Engineer jobs in Prosper, TX look for? The top searched job categories for Jr Data Engineer jobs in Prosper, TX are:
What cities near Prosper, TX are hiring for Jr Data Engineer jobs? Cities near Prosper, TX with the most Jr Data Engineer job openings:
Infographic showing various Jr Data Engineer job openings in Prosper, TX as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $118,793 per year, or $57.1 per hour.
Jr.Data Engineer

Full-time

Posted 27 days ago


Job description

Fresher JD
Mandatory:
· Solid understanding of data structures and algorithms (sort, select, search, trees, graphs, collections etc)
· Academic hands-on experience in Java or Scala or Python code (should be proficient)
· Academic hands-on experience in SQL (at least in one RDBMS: SQL Server or Postgres or MySQL or Oracle)
· Academic hands-on Linux experience (CentOS or Ubuntu)
· Good problem solving and analytical skillsNice to have:
· Knowledge of Azure
· Knowledge of Business Intelligence tools like Power BI
· Knowledge of Data Science
· Knowledge of distributed computing and/or massively parallel processing concepts and frameworks (at least one): Spark, Kafka, MapReduce, Impala
· Knowledge of Big Data technologies: Hadoop, HDFS, Hive, Impala, HBase, MongoDB, Cassandra, Kafka
· Knowledge of Spark and Spark SQL routines to process large volumes of data
· Knowledge of data warehousing and data modeling skills