Hire remote enterprise MongoDB developers

As a MongoDB developer at [Company], you will take care of MongoDB databases and work to improve their performance, security, and availability. Your responsibilities will include:

• Collaborate with the development teams to add new features to new products and apps and keep old ones running. 
• Write scripts to automate daily tasks while maintaining the security of your data sets.
• Contribute to code reviews and offer constructive feedback to team members.
• Assist the team in identifying and diagnosing technical issues and in resolving them.
• Keeping up-to-date with the latest developments in MongoDB and related technologies.

Requirements:

• Strong experience with a programming language commonly used with MongoDB (such as JavaScript, Python, or Java) is required.
• Understanding of database management systems
• Knowledge of either Mongoose or any other standard MongoDB driver.
• Ability to configure schema and MongoDB data modeling
• Expertise in MongoDB installation and administration on AWS and Red Hat
• Detailed knowledge of the MongoDB architecture
• Experience designing systems that manage large data sets and a massive volume of transactions.
• Expertise in database security administration
• Excellent analytic and problem-solving skills
• Strong communication skills, the ability to work well in a team, and the ability to problem-solve and troubleshoot

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Here are five questions that can be asked during a technical interview with MongoDB.

Question 1: How are queries performed in MongoDB?

This is a great question to find out the different ways a developer applies filtering to MongoDB queries in a project. A great response would be, "Querying can be done in MongoDB using the find method; however, the selection criteria are based on the query parameter passed in the find method." 

Code snippets could also be given for some of the criteria specified.

A good response would be to describe the process of querying without mentioning the selection criteria. As the interview goes on, the interviewer can ask how to filter and describe exactly what is being returned as results.

Question 2: What are indexes in MongoDB?

With this question, the interviewer wants to know if the developer understands the impact of the indexes, how indexes work, and how they are implemented in MongoDB. A good answer will be:

Indexes are structures in MongoDB that facilitate effective querying. Without indexes, MongoDB would have to look through every document in a collection to find the ones that match each query. The performance of your application may suffer as a result of these collection scans because they are slow. If you use an index to cut down on the number of documents that MongoDB has to look at, your queries will work better and return faster.

In MongoDB, there are many different types of indexes. The single field and compound index types are the two that are used the most frequently.

The best answer to this question is to include the impact of indexes on memory allocation. Before you build indexes, map out the types of queries you will run so that you can build indexes that reference those fields. Indexes come with a performance cost but are more than worth the cost for frequent queries on large data sets. Think about how often each query is used in the application and whether or not it needs an index.

Question 3: What is the difference between MapReduce and the Aggregation Pipeline in Data Aggregation in MongoDB?

This is a great question to ask to know if the developer understands the importance of efficiency and speed when querying large data sets.

A good answer would be that both methods of data aggregation are used to utilize stages to filter data and perform operations like grouping, sorting, and transforming the output of each operator. The queries for MapReduce are run in a single thread and are based on JavaScript using the SpiderMonkey engine. Aggregation Pipeline queries, on the other hand, are executed using compiled C++ code, which is faster than interpreted JavaScript.

A great answer will include the fact that MapReduce is no longer supported in MongoDB version 5.0 and give code samples for both methods. The developer should be able to write snippets that use the map-reduce method to query data and rewrite the operation as an aggregation pipeline.

A map-reduce operation on the collection of products sorts them by customer_id and figures out the total price for each customer_id:

An aggregation alternative to the map-Reduce operation:

Question 4: What are the important points to consider when creating a schema in MongoDB?

This is a great question for the interviewer to assess the developer's understanding of MongoDB schema design best practices.

Designing a schema that works for your application is one of the most crucial aspects. You can do that by taking into account user requirements, the data you want to store, the query performance, and the hardware that is available. If you intend to use multiple objects together, combine them into a single document; otherwise, separate them.

It is best to avoid creating documents that are likely to grow larger than the allowed default size of 16MB per document. Otherwise, queries may fail. When updating documents, use field modifiers to update only specific fields to avoid performance degradation caused by MongoDB having to rewrite the entire document, perform schema denormalization, and use embedded documents to retrieve the required data in a single query. You can create indexes to sort data, update them when documents are updated, and optimize their use by overriding the default id field.

A good answer will include the fact that a well-designed MongoDB schema is the most important part of setting up a database that is scalable, fast, and cheap.

Question 5: What is sharding in MongoDB?

MongoDB developers are primarily concerned with data storage and manipulation. This is a great question to test the developer's knowledge of data storage in MongoDB.

Sharding is the process of distributing and storing data records across multiple host machines in MongoDB. This helps keep high query rates from putting too much stress on the system and making it hard for users to use. For the sharding procedure, it is necessary to split large data sets into smaller ones.

MongoDB resolves this issue by using the horizontal form of scaling through sharding. Each part of a horizontally split database or search engine is called a "shard." Each shard is a single MongoDB instance, and it contains a sharded data subset. MongoDB creates a sharded cluster of multiple MongoDB instances with Mongo and at least three Config servers. This sharding approach allows MongoDB to scale up to meet data growth demands.

A great answer will include some considerations that need to be made when choosing to shard a data record. Sharded cluster infrastructure requirements and complexity require careful planning, execution, and maintenance.

MongoDB does not provide a way to unshard a sharded collection once it has been sharded.

Developers in the Torc community set their own rates. The three primary factors that determine the cost of a Remix developer are:

1. Experience - Everyone would prefer a senior developer but, let's be honest, 10+ years of experience isn't always required. If you consider the work the developer will be doing and the amount of support within your existing team they'll have access to, you might be better served with one or two junior Remix developers over one senior developer.
2. Skill mix - Marketplaces operate under normal supply and demand dynamics. Developer rates often increase with more niche, complex, or in-demand skill requirements.
3. Geography - While remote work has flattened the geographic pay disparity to some degree, where your developer lives does factor into their cost. In 2022, the median rate for Torc developers in Latin America was 29% lower than developers in the U.S., Canada, and Western Europe, while the median rates in Eastern Europe and Asia/Oceania were 24% and 40% lower, respectively.

Torc simplifies the hiring process for you and for developers, and it all starts with letting us know what you're looking for. If you're a new Torc customer, schedule a meeting with our sales team to discuss your needs and get the paperwork squared away. If you're an exiting customer, you can create a new job opportunity directly in the Torc platform.

Once the job is finalized, our AI matching algorithm and team of technical talent specialists will get to work identifying great candidates from the Torc community. You can review their profiles and assessments, interview shortlisted candidates, and hire the perfect developer - all in under a week.