Hire Hadoop/Kafka Data Engineers
Hadoop is an open-source software framework that facilitates storage and processing of large datasets, particularly those on commodity hardware clusters in a distributed computing system. Hadoop simplifies the distribution of computations across multiple machines, making it easier to manage enormous datasets. This technology has become the essential element for handling large data systems, thus playing a key role in multiple Internet-based applications.
Apache Kafka, a well-known open-source event streaming platform written in Java and Scala, has been gaining traction among developers for its wide range of uses, such as data integration, analytics, high-performance data pipelines, and mission-critical applications. As a result, companies have started to employ Kafka engineers in the recent years in order to leverage the advantages of this technology.
What are the responsibilities of Hadoop/Kafka data engineers?
Many of the world’s most prominent companies, such as Netflix, LinkedIn, Uber, and automobile manufacturers, have come to rely on Apache Kafka for their data streaming needs. As an open-source platform, Apache Kafka has been designed to offer a message queue for handling billions of events per day. Developers are making use of Kafka’s features to build real-time streaming pipelines and applications that can process and analyse data as it is received.
By leveraging Hadoop to transform data into actionable insights, organisations can create more personalised experiences to target their customers. By gaining an understanding of their customers, businesses can develop effective advertising, marketing, and other customer-attracting initiatives that will give them a competitive advantage in the market. By utilising Hadoop’s capabilities to effectively transform data into meaningful information, businesses can ensure that they are in the best possible position to attract and retain customers.
It is fair to predict that Hadoop/Kafka data engineers will remain in high demand.
What are the duties and obligations of a Hadoop/Kafka data engineer?
As a Hadoop Developer, one is responsible for developing and programming Hadoop applications that are capable of managing and storing large data sets. The ideal candidate should possess extensive experience in setting up, running and debugging Hadoop clusters. Furthermore, they should have knowledge of end-to-end implementation and production of different data projects, developing and upgrading web applications, performing independent functional and technical research for various projects, and should be familiar with the functional programming methodology, containers, container orchestration, deploying cloud-native applications, Behaviour Driven Development and Test Driven Development. The job responsibilities of a Data Engineer who works with Hadoop/Kafka include creating a multi-data centre (MDC) Kafka deployment strategy, managing containers, and deploying cloud-native applications. It is essential for companies seeking to recruit Hadoop Developers to look for experienced professionals who can meet their demands of setting up and managing large-scale data storage and processing infrastructure.
- Create data analytics apps with high performance and minimal latency.
- Using data pipelines, you may automate the synchronisation and processing of complicated data streams.
- In collaboration with data scientists/engineers, designers, and front-end developers, create data processing and storage components.
- Create relational database models and include rigorous testing to assure high-quality output.
- By importing data from diverse databases, you may help the documentation team provide effective customer documentation.
- Contribute to the creation of analytic data assets and the implementation of modelled properties.
How does one go about becoming a Hadoop/Kafka data engineer?
When searching for a position as a Hadoop/Kafka data engineer, it is important to consider the level of education that is required. A high school diploma alone is not generally deemed sufficient for obtaining a job in this field; applicants who possess a Bachelor’s or Master’s degree tend to be the most attractive candidates for such a role.
In order to succeed in your profession, gaining practical experience and developing expertise is paramount. One way to acquire such knowledge is through internships. Accreditation is also essential, as it can demonstrate your dedication to the field and your proficiency, setting you apart from non-certified Hadoop/Kafka data engineers. Furthermore, it can provide access to more promising opportunities and enable you to progress in your career as a Hadoop/Kafka data engineer and ultimately, flourish in the industry.
The following are some of the most significant hard skills required by a Hadoop/Kafka data engineer to succeed in the workplace:
Qualifications for becoming a Hadoop/Kafka data engineer
In order to be successful in acquiring high-paying Hadoop/Kafka data engineer employment, it is essential for one to possess certain skills and understanding of the fundamentals. As such, those interested in this field should begin their preparation by learning the necessary abilities that will allow them to be successful in their pursuit of employment. Here is the information one needs to know in order to be successful.
Understanding of the Apache Kafka architectureUnderstanding the architecture of Apache Kafka is highly advantageous. Despite its initial complexity, the architecture is quite simple and allows for the quick, efficient transmission of messages within applications. Because of this, Apache Kafka is highly valued for its speed and versatility.
APIs for KafkaA Hadoop/Kafka data engineer should possess extensive knowledge of four Java APIs: the producer API, consumer API, streams API, and connector API. This expertise is essential in order to configure Kafka as an effective platform for stream processing applications. The streams API grants the engineer high-level control over the processing of data streams, while the connectors API allows them to create reusable data import and export connectors. Furthermore, it is recommended that the engineer has a comprehensive understanding of other related abilities.
Hadoop FundamentalsIn order to successfully prepare for a remote Hadoop/Kafka data engineering position, it is essential to have an in-depth knowledge and understanding of the technology. It is necessary to gain a fundamental understanding of the capabilities, uses, advantages and disadvantages of Hadoop before progressing onto more complex topics. To best equip oneself for this, one should refer to a variety of different sources of information, including tutorials, journals, research papers, seminars and other similar activities both online and offline.
SQLIn order to become a successful data engineer with expertise in Hadoop and Kafka, it is essential to have an in-depth understanding of Structured Query Language (SQL). Having knowledge of other query languages such as HiveQL will be extremely advantageous, provided a robust command of SQL is already established. Additionally, it is recommended to broaden one’s expertise by reviewing database fundamentals, distributed systems, and other pertinent topics.
Components of HadoopNow that you have acquired a basic understanding of the principles of Hadoop and the technical expertise required to utilise it, it is time to gain a better insight into the Hadoop ecosystem as a whole. The Hadoop ecosystem is composed of four vital components: Map-Reduce, Hadoop Distributed File System, YARN (Yet Another Resource Negotiator), and Hadoop Common. Map-Reduce is a programming model that enables efficient processing of large datasets across clusters of computers. The Hadoop Distributed File System (HDFS) is a distributed file-system designed to store data on commodity hardware. YARN is a software framework for managing computational resources for distributed applications and Hadoop Common is a library of utilities designed to facilitate the development of Hadoop-based applications.
Where can I get remote Hadoop/Kafka data engineer jobs?
In order to excel as a Hadoop/Kafka data engineer, it is essential to train diligently and consistently, similar to the way athletes approach their sport. To maintain success and avoid burnout, two key factors must be taken into account: the guidance of a more experienced professional and the implementation of effective practice strategies. Understanding the amount of practice and effort required is key to achieving success, and it is important to have an experienced mentor to monitor your progress and alert you to signs of burnout.
At Works, we are committed to providing the highest quality remote Hadoop/Kafka data engineers to meet your career objectives. Our experienced engineers are ready to tackle challenging technical and business problems with the most modern technology, helping you to reach your goals faster. By joining our team of the most talented developers, you will be able to secure full-time, long-term remote Hadoop/Kafka data engineer positions that offer greater remuneration and better prospects for career progression.
Responsibilities at work
- Create low-latency, high-performance data analytics apps.
- Create automated data pipelines to synchronise and process large amounts of data.
- Create data processing and data storage components in collaboration with data scientists/engineers, front-end developers, and designers.
- To offer high-quality products, create data models for relational databases and build rigorous integration tests.
- Participate in the loading of data from several distinct datasets, and aid the documentation team in generating excellent client documentation.
- Contribute to the scope and design of analytic data assets, as well as the implementation of modelled properties.
- Engineering or computer science bachelor’s/master’s degree (or equivalent experience)
- 3+ years of data engineering experience (rare exceptions for highly skilled developers)
- Extensive knowledge of large data technologies such as Hadoop, Hive, Druid, and others.
- Expertise in building and maintaining large data pipelines utilising Kafka, Flume, Airflow, and other tools.
- Working efficiently with Python and other data processing languages such as Scala, Java, and others.
- Working knowledge of AWS hosted environments
- Database expertise, including SQL, MySQL, and PostgreSQL
- Knowledge of DevOps environments and containerization (Docker, Kubernetes, etc.)
- To communicate successfully, you must be fluent in English.
- Work full-time (40 hours per week) with a 4-hour overlap with US time zones
- Knowledge of machine-learning systems
- Understanding of batch data processing and the development of real-time analytic systems
- practical experience with Golang and Scala
- Understanding of systems that are highly distributed, scalable, and have minimal latency
- Data visualisation and BI solutions such as Power BI, Tableau, and others
- REST API development experience
- Outstanding organisational and communication abilities
- Excellent technical, analytical, and problem-solving abilities