Data Engineer Salary at Kong Co BETA

How much does a Kong Co Data Engineer make?

As of April 2025, the average annual salary for a Data Engineer at Kong Co is $115,033, which translates to approximately $55 per hour. Salaries for Data Engineer at Kong Co typically range from $104,347 to $123,916, reflecting the diverse roles within the company.

It's essential to understand that salaries can vary significantly based on factors such as geographic location, departmental budget, and individual qualifications. Key determinants include years of experience, specific skill sets, educational background, and relevant certifications. For a more tailored salary estimate, consider these variables when evaluating compensation for this role.

DISCLAIMER: The salary range presented here is an estimation that has been derived from our proprietary algorithm. It should be noted that this range does not originate from the company's factual payroll records or survey data.

Website:
kongcompany.com
Size:
100 - 200 Employees
Revenue:
$10M - $50M
Industry:
MFG Durable

Kong Company, LLC provides pet toys. It offers grooming products and e-collars, knots, treats, plush toys, and rubber toys for dogs and puppies; and active toys, grooming products and e-collars, refillable catnip toys, cat naturals, and treat dispensers for cats and kittens. Kong Company, LLC was founded in 1976 and is based in Golden, Colorado.

See similar companies related to Kong Co

What Skills Does a person Need at Kong Co?

At Kong Co, specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.

  1. SQL: Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS).
  2. Python: Applying the concepts and algorithms of Python to design, develop and maintain software applications to comply with business requirements.
  3. Data engineering: Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale
  4. AWS: Amazon Web Services, Inc. is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis.
  5. Big Data: Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed to big data are veracity (i.e., how much noise is in the data) and value. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

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Check more jobs information at Kong Co

Job Title Average Kong Co Salary Hourly Rate
2 Warehouse Worker $31,990 $15
3 Assembler $35,469 $17
4 Assistant Manager $90,967 $44
5 Associate Account Manager $94,225 $45
6 Director, Strategic Accounts $178,796 $86
7 Ecommerce $37,156 $18
8 Group Product Manager $124,668 $60
9 Head of Product Marketing $211,286 $102
10 HR Business Partner $91,268 $44
11 Manager, Manufacturing $112,164 $54
12 Manager, Marketing Programs $105,998 $51

Hourly Pay at Kong Co

The average hourly pay at Kong Co for a Data Engineer is $55 per hour. The location, department, and job description all have an impact on the typical compensation for Kong Co positions. The pay range and total remuneration for the job title are shown in the table below. Kong Co may pay a varying wage for a given position based on experience, talents, and education.
How accurate does $115,033 look to you?

FAQ about Salary and Jobs at Kong Co

1. How much does Kong Co pay per hour?
The average hourly pay is $55. The salary for each employee depends on several factors, including the level of experience, work performance, certifications and skills.
2. What is the highest salary at Kong Co?
According to the data, the highest approximate salary is about $123,916 per year. Salaries are usually determined by comparing other employees’ salaries in similar positions in the same region and industry.
3. What is the lowest pay at Kong Co?
According to the data, the lowest estimated salary is about $104,347 per year. Pay levels are mainly influenced by market forces, supply and demand, and social structures.
4. What steps can an employee take to increase their salary?
There are various ways to increase the wage. Level of education: An employee may receive a higher salary and get a promotion if they obtain advanced degrees. Experience in management: an employee with supervisory experience can increase the likelihood to earn more.