Big Data Engineers

Hire Big Data Engineers

In the current business landscape, data has become an integral part of any successful venture. As the emphasis on data increases, businesses are increasingly looking for professionals that possess a combination of IT and Data Science skills to help them gain meaningful insights from the accessible resources. Therefore, having a deep understanding of data-related topics is becoming increasingly important for professionals looking to stay ahead of the competition.

Big Data is a powerful tool, consisting of immense amounts of data that require sophisticated computing capabilities to analyse and draw meaningful insights from. This type of data can be leveraged by businesses to gain invaluable insights that can be used to inform decisions and propel the organisation’s growth, such as by improving security measures.

When it comes to uncovering functional patterns and generalisations within Big Data, there remains no more effective technique than leveraging the expertise of an industry professional. This has prompted many businesses to search for trained and experienced Big Data engineers who possess hands-on knowledge of the field. As a result, remote Big Data engineer positions are becoming increasingly sought-after.

What does Big Data development entail?

Big Data has had a significant impact on a variety of areas, from consumer behaviour to economic research to political campaigns to healthcare. By collecting and analysing data from activities conducted online, such as driving a car, browsing the internet, and taking part in classroom activities, organisations of all sizes have been able to gain insight into the needs and preferences of their customers throughout the year. Moreover, Big Data has allowed us to leverage advanced analytics to make predictions about future events before they occur.

What are the duties and obligations of a Big Data engineer?

Data analysts and Big Data engineers are similar in many respects, as they both specialise in researching and critically analysing large datasets. Both roles require the ability to draw meaningful insights from the data, as well as the skill to communicate their findings to stakeholders in a clear and concise way. Furthermore, these professionals must have the capacity to manipulate and manage huge data sets, ensuring all information is accurate and up-to-date.

The duties of their role also involve articulating the requirements of business initiatives after investigating current methods and providing assistance in presenting potential options to corporate stakeholders. As an example, Big Data engineers are accountable for certain aspects of databases such as query analysis, database designs, performance analysis, and related activities such as security measures to protect against unauthorised access.

Big Data engineers are responsible for a variety of tasks:

• Hadoop design, construction, configuration, and support
• Maintain data security and privacy.
• Analyse several data repositories to find insights
• Convert complicated functional and technological requirements into intricate designs
• Create high-performance, scalable web services for data tracking.
• Change the designs of numerous procedures.
• Create and deploy Hadoop
• Learn how to process data in parallel.

How does one go about becoming a Big Data engineer?

It is possible to become a Big Data engineer without any formal schooling. Regardless of your educational background or experience, with the right knowledge and hands-on experience, you can build a successful career in this field. Though having a bachelor’s or master’s degree in computer science or similar discipline is not a requirement to be considered for remote Big Data engineer positions, it can be beneficial. A technical degree can provide you with a solid understanding of programming and web development, as well as give you the opportunity to advance in your career and expand your job prospects. When applying for Big Data engineer positions, it is important to create a resume that properly showcases your abilities and experiences. Doing so can make a strong impression on the recruiter or hiring manager.

We’ve compiled a list of important skills for becoming a professional Big Data developer.

Qualifications for becoming a Big Data engineer

The initial step towards obtaining a lucrative job as a remote Big Data engineer is to begin learning the essential competencies necessary for success. It is important to go through all of the information you need to be aware of in order to make sure that you are well-prepared for your new role. With the proper knowledge and expertise, you can confidently excel in the world of Big Data engineering and secure the job you desire.

1. Apache Hadoop

   Hadoop is a widely accepted open-source solution for dealing with Big Data. It is characterised by its capability to distribute complicated calculations over numerous workstations within a cluster. This is facilitated by its primary tool, Map Reduce, and the cluster administration feature which Hadoop provides. The data is split into large batches that are sent across the network to smaller sub-processes. These sub-processes then recombine the data and produce a comprehensive output.

2. Spark

   Spark has several advantages over Hadoop and the MapReduce model. Unlike Hadoop, Spark operates in memory, which leads to faster processing times. Additionally, Spark’s data flow is not limited to the linear structure of Hadoop’s MapReduce, allowing for more flexibility in pipeline construction.

3. Flink

   Flink is a dataflow engine that is specially designed to process data streams, leveraging the strengths of both batch processing and real-time streaming. As a result, there is no distinction between stream and batch applications when using Flink, as both are treated as streams. To make it easier to use, Flink provides streaming APIs specifically tailored for Java, Scala, Python, and other programming languages. Additionally, Flink is highly performant and has minimal latency, further making it a great choice for stream processing.

4. Samza

   Apache Samza is a distributed stream processing framework that takes advantage of Apache Kafka for communications and YARN for cluster resource management. It is designed to be long-lasting, easily expandable, and highly pluggable. Furthermore, Samza offers an intuitive, callback-based “process message” API which is easier to use than the MapReduce API. Additionally, Samza relies on Kafka to guarantee that messages are processed in the same order that they were sent to a partition, and that no messages are discarded.

5. Storm

   Apache Storm is a powerful real-time distributed processing system designed for the rapid and efficient handling of unbounded streams. It allows for the construction of applications in the form of directed acyclic graphs and can be used with any programming language, making it highly versatile. Furthermore, it is impressively scalable, being capable of processing over one million tuples per second per node – making it an ideal choice for a broad range of tasks such as real-time analytics and distributed machine learning.

6. SQL

   Gaining an understanding of Structured Query Language (SQL) is an essential step in the journey to becoming a Big Data engineer, as it serves as the foundations of data-centric programming and working with Big Data technologies such as NoSQL (Not only SQL). As such, a solid understanding of SQL is paramount to success in this field.

7. Data Mining

   Data mining is a strategy for discovering meaningful patterns, descriptive models, and useful insights from large data sets. It is the process of extracting useful information from a substantial amount of data. In a large relational database, data mining can be used to find patterns or correlations between hundreds of variables. The aim of data mining is usually categorization or prediction.

Where can I get remote Big Data engineer jobs?

As engineers, it is essential that we practice efficiently and regularly in order to build our skills and progress in our profession. To ensure that we are on the right track, we must seek guidance from experienced professionals and be mindful of tasks that may be causing burnout. This includes seeking assistance with practice procedures and monitoring our workload to ensure that we are dedicating adequate time to hone our abilities. Only then can we expect to see steady advancement in our abilities and capabilities.

At Work, we offer the most exceptional remote Big Data engineer jobs that can help you progress in your career as a Big Data engineer. You will have the chance to expand your knowledge and capabilities by tackling complex technical and commercial problems with the latest technologies available. Furthermore, by joining our network of highly accomplished engineers, you can find full-time, long-term remote Big Data engineer positions with improved remuneration and multiple opportunities to grow professionally.

Job Description

Responsibilities at work

• Choose and integrate the necessary Big Data technologies and frameworks to supply the essential competencies.
• Assemble, process, and analyse raw data at scale to meet the needs of various projects.
• Monitor data performance and make necessary infrastructure changes.
• Maintain production systems and establish data preservation policies.
• Collaboration with internal development and research teams is essential.
• Handle technical discussions with internal operations and survey suppliers.
• Collaborate directly with the internal development and research team on web scraping, API calls, and SQL query creation.
• Investigate innovative ideas for resolving data mining difficulties based on industry best practices, data revisions, and expertise.

Requirements

• Bachelor’s/degree Master’s in Computer Science, Computer Engineering, Data Science, or a related field is required.
• 3+ years of proven experience as a data engineer (rare exceptions for super efficient devs)
• A keen understanding of distributed computing concepts
• Expertise in data mining, machine learning, and information retrieval
• Hadoop, Spark, and other related frameworks expertise
• Understanding of Lambda architecture, its pros and drawbacks
• Knowledge of numerous programming languages, such as Java, C++, Linux, PHP, or Python
• Knowledge of numerous ETL approaches and frameworks

Preferred Skills

• Willingness to troubleshoot complicated data, software, and networking problems
• Working knowledge of Cloudera, Hortonworks, or MapR
• Experience integrating data from diverse sources
• Knowledge of RDBMS and NoSQL databases
• Experience with data lakes Excellent troubleshooting and project management abilities