A Data Infrastructure Engineer is responsible for designing, implementing, and maintaining data pipelines, storage solutions, and large-scale processing systems to facilitate the seamless flow and analysis of big data. The role typically requires expertise in cloud computing, networks, and databases.
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Data Infrastructure Engineer
$103,143 / year
The average salary for Data Infrastructure Engineer is $103,143 / year according to Glassdoor.com
There are no updated reports for Data Infrastructure Engineer salaries. You can check potential lateral job opportunities in this information stack to find related salary information.
Data Infrastructure Engineer role may have an alternate title depending on the company. To find more information, you can check Glassdoor.com.
As a Data Infrastructure Engineer, you will be responsible for designing and managing the infrastructure that supports an organization's data systems. You will need a deep understanding of database technologies, as well as experience with cloud platforms and infrastructure management tools. Strong problem-solving and troubleshooting skills are essential, as you will be responsible for optimizing data infrastructure performance and resolving any issues that arise.
The following text about the Job role of Data Infrastructure Engineer has been generated by an AI model developed by Cohere. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.
The role of a Data Infrastructure Engineer is to design, build, and maintain the infrastructure that supports data storage, processing, and analysis. They are responsible for ensuring that data is stored securely, accessible, and available for analysis by data scientists and other stakeholders.
One of the most important skills for a Data Infrastructure Engineer is expertise in data storage technologies. They need to have a deep understanding of databases, both relational and non-relational, and be able to design and implement efficient data storage solutions. This includes knowledge of database management systems such as MySQL, PostgreSQL, MongoDB, and Cassandra.
Another crucial skill for a Data Infrastructure Engineer is proficiency in data processing frameworks. They need to be familiar with technologies such as Apache Hadoop, Apache Spark, and Apache Kafka. This includes the ability to design and implement data processing pipelines, perform data transformations, and optimize data processing performance.
In addition to these skills, a Data Infrastructure Engineer should have experience with cloud computing platforms such as AWS or Azure. They need to be able to design and deploy data infrastructure solutions in the cloud, manage cloud resources, and ensure the scalability and reliability of the infrastructure.
Data security and privacy are also important aspects of the job role. A Data Infrastructure Engineer needs to have knowledge of data security best practices and be able to implement security measures to protect sensitive data. This includes encryption, access controls, and monitoring for data breaches.
Furthermore, a Data Infrastructure Engineer should have strong problem-solving and troubleshooting skills. They need to be able to identify and resolve issues with data infrastructure, such as performance bottlenecks or data corruption. They also need to be able to work collaboratively with other teams, such as data scientists or software engineers, to address data infrastructure needs.
Overall, a Data Infrastructure Engineer plays a critical role in designing and maintaining the infrastructure that supports data storage, processing, and analysis. They need to possess strong skills in data storage, data processing, cloud computing, and data security. By leveraging these skills, they can ensure that data is accessible, secure, and available for analysis, enabling organizations to make data-driven decisions.
Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.
Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.
CKA: Certified Kubernetes Administrator
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If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.
MLOps course for data scientists & ML engineers covers basics to advanced stages, including ML lifecycle, feature stores, CI/CD pipelines, and containerization, enabling efficient and consistent ML deployment.
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Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.
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