Pay
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Reads like a Data Engineer role: pipelines and data infrastructure.
Design, build, and maintain ETL data pipelines using SQL and Python
Develop and maintain backend services and APIs for data access and integration
Build and enhance BI dashboards (e.g., Tableau) for data accuracy and adoption
Translate operational user needs into system designs and reporting specifications
Monitor data quality and system reliability, resolving issues that affect users
Design, build, and maintain reliable data pipelines (ETL) using SQL and Python
Develop and maintain backend services and APIs to support data access, system integration, and application functionality
Build and enhance BI dashboards and visualizations (e.g., Tableau) with a strong focus on data accuracy, usability, and real-world adoption
translate requirements into system designs, data pipelines, and reporting specifications
Proactively monitor data quality and system reliability, identifying and resolving issues that impact user experience
Strong SQL skills; experience participating in or being familiar with database schema design
Proficiency in Python, SQL, and Git, with experience applying them in real projects
Backend development experience; experience participating in or being familiar with building APIs in Python
Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets
the ability to clearly explain technical solutions and trade-offs
Job Summary This role is a software- and data engineering–focused position responsible for turning real-world user needs into production-ready data pipelines, backend services, and data-driven applications. The primary focus is on data processing, system integration, performance, and reliability, working closely with business and operational users to continuously improve systems and dashboards so that data can be accurately and effectively used in real operational scenarios. This role emphasizes building and operating reliable data systems rather than research-oriented modeling or algorithm development. Key Responsibilities ●Work closely with internal users (e.g., operations, manufacturing, or business teams) to understand real usage scenarios and translate requirements into system designs, data pipelines, and reporting specifications ●Design, build, and maintain reliable data pipelines (ETL) using SQL and Python ●Develop and maintain backend services and APIs to support data access, system integration, and application functionality ●Build and enhance BI dashboards and visualizations (e.g., Tableau) with a strong focus on data accuracy, usability, and real-world adoption ●Collaborate with engineers, IT teams, and cross-functional partners on system integration and workflow optimization ●Optimize system performance and architecture as data volume and usage patterns evolve ●Proactively monitor data quality and system reliability, identifying and resolving issues that impact user experience ●Collaborate with global engineering and data teams, including partners in Asia, to align on system design, data workflows, and implementation details. This role values production-ready systems that are stable, accurate, and actually used, rather than model-centric or research-driven solutions. Qualifications Must Have ●Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets ●Proficiency in Python, SQL, and Git, with experience applying them in real projects ●Strong SQL skills; experience participating in or being familiar with database schema design, with an understanding of how data models support usage scenarios ●Backend development experience; experience participating in or being familiar with building APIs in Python to support system integration and data access ●Experience working with cross-functional teams and the ability to clearly explain technical solutions and trade-offs ●Experience collaborating with users to translate real-world requirements into deployable systems, data workflows, or dashboards
Job Summary This role is a software- and data engineering–focused position responsible for turning real-world user needs into production-ready data pipelines, backend services, and data-driven applications. The primary focus is on data processing, system integration, performance, and reliability, working closely with business and operational users to continuously improve systems and dashboards so that data can be accurately and effectively used in real operational scenarios. This role emphasizes building and operating reliable data systems rather than research-oriented modeling or algorithm development. Key Responsibilities ●Work closely with internal users (e.g., operations, manufacturing, or business teams) to understand real usage scenarios and translate requirements into system designs, data pipelines, and reporting specifications ●Design, build, and maintain reliable data pipelines (ETL) using SQL and Python ●Develop and maintain backend services and APIs to support data access, system integration, and application functionality ●Build and enhance BI dashboards and visualizations (e.g., Tableau) with a strong focus on data accuracy, usability, and real-world adoption ●Collaborate with engineers, IT teams, and cross-functional partners on system integration and workflow optimization ●Optimize system performance and architecture as data volume and usage patterns evolve ●Proactively monitor data quality and system reliability, identifying and resolving issues that impact user experience ●Collaborate with global engineering and data teams, including partners in Asia, to align on system design, data workflows, and implementation details. This role values production-ready systems that are stable, accurate, and actually used, rather than model-centric or research-driven solutions. Qualifications Must Have ●Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets ●Proficiency in Python, SQL, and Git, with experience applying them in real projects ●Strong SQL skills; experience participating in or being familiar with database schema design, with an understanding of how data models support usage scenarios ●Backend development experience; experience participating in or being familiar with building APIs in Python to support system integration and data access ●Experience working with cross-functional teams and the ability to clearly explain technical solutions and trade-offs ●Experience collaborating with users to translate real-world requirements into deployable systems, data workflows, or dashboards Nice to Have ●Experience with Linux and Docker, including system deployment or operations ●Familiarity with CI/CD pipelines ●Experience with database performance tuning or large-scale data processing ●Ability to communicate in Spanish or Chinese for work-related discussions ●Comfortable working in a global, cross-time-zone collaboration environment
Qualifications Must Have ●Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets ●Proficiency in Python, SQL, and Git, with experience applying them in real projects ●Strong SQL skills; experience participating in or being familiar with database schema design, with an understanding of how data models support usage scenarios ●Backend development experience; experience participating in or being familiar with building APIs in Python to support system integration and data access ●Experience working with cross-functional teams and the ability to clearly explain technical solutions and trade-offs ●Experience collaborating with users to translate real-world requirements into deployable systems, data workflows, or dashboards Nice to Have ●Experience with Linux and Docker, including system deployment or operations ●Familiarity with CI/CD pipelines ●Experience with database performance tuning or large-scale data processing ●Ability to communicate in Spanish or Chinese for work-related discussions ●Comfortable working in a global, cross-time-zone collaboration environment
Open 5 monthsWe first saw this posting 154 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Day 154
Up longer than most. Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
5 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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Houston
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