Data Engineer
RxCloud
Remote (US) · Posted October 15, 2026
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Pay compared with the market
This listing does not state pay. Across 143 open data engineer listings that state pay, the middle half runs $112,500 to $162,500 and the median is $137,500.
Listed pay is what employers advertise, not what people are paid.
How AI is used in this kind of work
Among database architects, AI is already used for a large share of the tasks. Measured exposure for database architects: 58% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
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About the job
Data Engineer Job Description
A Data Engineer is a data professional who uses their expertise in data engineering and programming to build systems that collect, manage, and convert raw data into usable information for business analysts.
Requirements and skills
Previous 7+ years of experience as a data engineer or in a similar role
Technical expertise with data models, data mining, and segmentation techniques
Knowledge of programming languages (e.g., Java and Python)
Hands-on experience with SQL database design
Great numerical and analytical skills
Degree in Computer Science, IT, or similar field; a masters is a plus.
Data engineering certification (e.g., IBM Certified Data Engineer) is a plus.
Big data technologies such as Hadoop, Spark, and Kafka.
Also be familiar with cloud platforms such as AWS, Google Cloud, and Azure.
Should be proficient in SQL, Excel, Tableau, or other BI tools. They should also have a good understanding of statistical analysis and modelling techniques, as well as business acumen.
Responsibilities
Analyse and organize raw data.
Build data systems and pipelines.
Evaluate business needs and objectives.
Interpret trends and patterns.
Conduct complex data analysis and report on results.
Prepare data for prescriptive and predictive modelling.
Build algorithms and prototypes.
Combine raw information from different sources.
Explore ways to enhance data quality and reliability.
Identify opportunities for data acquisition.
Develop analytical tools and programs.
Collaborate with data scientists and architects on several projects.