Looking for Skilled Data Engineers Specialising in Hadoop/Kafka
Looking for a way to handle large datasets? Look no further than Hadoop. Developed as an open-source software framework, Hadoop simplifies the storage and processing of large datasets, making it an essential component for multiple Internet-based applications. Its ability to distribute computations across multiple machines makes it an invaluable tool for managing enormous datasets, particularly those on commodity hardware clusters in a distributed computing system.
Developers are increasingly turning to Apache Kafka, an open-source event streaming platform written in both Java and Scala, for its versatility in integration, analytics, high-performance data pipelines, and mission-critical applications. In response, companies are hiring Kafka engineers to make the most of this technology’s advantages.
What Does the Role of a Hadoop/Kafka Data Engineer Entail?
Leading companies such as Netflix, LinkedIn, Uber, and major automobile manufacturers have all begun to rely on Apache Kafka for their data streaming requirements. Thanks to its open-source platform, Apache Kafka provides a message queue capable of handling billions of events per day. Developers can utilize Kafka’s features to create real-time streaming pipelines and applications designed to process and analyze data as it arrives.
By utilising Hadoop to transform data into actionable insights, organisations can provide more personalised experiences for their customers. This deeper understanding of customers can lead to effective advertising, marketing, and other attractive initiatives, providing businesses with a competitive edge in the market. By harnessing Hadoop’s data transformation capabilities, businesses can ensure they are ideally placed to attract and retain customers by turning data into actionable information.
It is safe to assume that demand for Hadoop/Kafka data engineers will remain consistently high.
What Are the Responsibilities of a Hadoop/Kafka Data Engineer?
Hadoop Developers are responsible for developing and programming Hadoop applications capable of managing and storing large datasets. An ideal candidate should have extensive experience in setting up, running and debugging Hadoop clusters. They should possess knowledge of end-to-end implementation and production of different data projects, upgrading web applications, conducting functional and technical research, and understanding functional programming methodology, containers, container orchestration, deploying cloud-native applications, Behaviour Driven Development, and Test Driven Development. As for Data Engineers working with Hadoop/Kafka, their job responsibilities include creating an MDC Kafka deployment strategy, managing containers, and deploying cloud-native applications. Companies looking to hire Hadoop Developers should seek experienced professionals capable of setting up and managing large-scale data storage and processing infrastructure.
- Develop data analytics applications with a focus on high performance and minimal latency.
- Automate the synchronization and processing of complex data streams using data pipelines.
- Collaborate with front-end developers, designers, and data scientists/engineers when creating data processing and storage components.
- Develop relational database models and rigorously test them to ensure high-quality output.
- Assist the documentation team in creating effective customer documentation by importing data from various databases.
- Participate in the development of analytic data assets and the implementation of modelled properties.
What Are the Steps to Becoming a Hadoop/Kafka Data Engineer?
Education level plays a crucial role in securing a position as a Hadoop/Kafka data engineer. Generally, a high school diploma is not sufficient, and candidates with a Bachelor’s or Master’s degree are more attractive to potential employers in this field.
Practical experience and expertise are vital to succeeding as a Hadoop/Kafka data engineer. Internships are a great way to gain knowledge and develop skills. Accreditation is also essential as it demonstrates your dedication to the field and proficiency, distinguishing you from non-certified Hadoop/Kafka data engineers, providing access to better opportunities, and enabling career growth in the industry.
Below are some of the crucial hard skills a Hadoop/Kafka data engineer needs to possess to excel in their job:
Requirements to Become a Hadoop/Kafka Data Engineer
To land highly-paid employment as a Hadoop/Kafka data engineer, possessing specific skills and understanding of the fundamentals is crucial. Aspiring professionals should start by learning the essential abilities required to succeed in this field to prepare themselves effectively for employment opportunities. The following information is essential for success.
Comprehension of Apache Kafka Architecture
Having a solid understanding of Apache Kafka architecture is immensely beneficial. Despite its apparent complexity, the architecture is straightforward and facilitates swift and efficient message transmission within applications. This is why Apache Kafka is renowned for its versatility and speed.Kafka APIs
A Hadoop/Kafka data engineer must have thorough knowledge of four Java APIs: the producer API, consumer API, streams API, and connector API. This expertise is crucial to configure Kafka as an effective platform for stream processing applications. The streams API provides high-level control over data stream processing, while the connectors API enables engineers to create reusable data import and export connectors. Additionally, it is advisable for the engineer to possess a comprehensive understanding of other relevant skills.Understanding of Hadoop Basics
To prepare oneself effectively for a remote Hadoop/Kafka data engineering role, proficiency in the technology is vital. A foundational understanding of Hadoop’s capabilities, applications, benefits, and drawbacks is essential before moving on to more advanced topics. To acquire this knowledge, one should consult a range of sources such as tutorials, journals, research papers, seminars, and related online and offline activities.SQL Proficiency
A Hadoop/Kafka data engineer needs to possess an in-depth understanding of Structured Query Language (SQL) to excel in their role. Being familiar with other query languages like HiveQL can be highly advantageous, but only after mastering SQL. Moreover, the engineer should expand their knowledge base by reviewing subjects such as distributed systems, database fundamentals, and other relevant topics.Hadoop Components
After obtaining a basic comprehension of Hadoop fundamentals and the technical expertise needed to work with it, the next step is gaining a thorough understanding of the Hadoop ecosystem. The Hadoop ecosystem comprises four critical components: MapReduce, Hadoop Distributed File System, YARN (Yet Another Resource Negotiator), and Hadoop Common. MapReduce is a programming model that facilitates efficient processing of vast datasets over computer clusters. Hadoop Distributed File System (HDFS) is a distributed file system created to store data on inexpensive hardware. YARN is a software framework designed to manage computational resources for distributed applications, while Hadoop Common is a set of utilities that helps in the development of Hadoop-based applications.
Where to Find Remote Hadoop/Kafka Data Engineer Jobs?
Becoming a successful Hadoop/Kafka data engineer requires consistent and rigorous training, just like athletes approach their sport. To avoid burnout and ensure continued success, two crucial factors must be considered: guidance from a seasoned professional and the adoption of effective practice strategies. Realizing the effort and practice required is essential to achieving success, and having an experienced mentor to monitor progress and prevent burnout is crucial.
At Works, we are dedicated to providing top-notch remote Hadoop/Kafka data engineers who can help you achieve your career objectives. Our skilled engineers are equipped to handle complex technical and business challenges using cutting-edge technology, enabling you to attain your goals faster. By joining our team of exceptional developers, you can secure full-time, long-term remote Hadoop/Kafka data engineer positions that offer higher remuneration and better opportunities for career advancement.
Job Responsibilities
Work Responsibilities
- Develop high-performance data analytics applications with low latency.
- Build automated data pipelines for synchronizing and processing massive volumes of data.
- Collaborate with data scientists/engineers, front-end developers, and designers to create data processing and data storage components.
- Develop data models for relational databases and conduct comprehensive integration tests to ensure high-quality products.
- Contribute to loading data from multiple disparate datasets and assist the documentation team in creating top-notch client documentation.
- Play a role in defining the scope and design of analytical data assets, as well as in implementing modeled properties.
Requirements
- Bachelor’s/Master’s degree in Engineering or Computer Science (or equivalent experience)
- Minimum of 3 years of experience in data engineering (with rare exceptions for exceptionally skilled developers)
- Proficiency in large data technologies like Hadoop, Hive, Druid, and other related tools.
- Mastery in constructing and managing massive data pipelines using tools such as Kafka, Flume, Airflow, and others.
- Demonstrated proficiency in working efficiently with Python and other data processing languages such as Scala, Java, and others.
- Familiarity with AWS hosted environments
- Proficiency in databases, including SQL, MySQL, and PostgreSQL
- Understanding of DevOps environments and containerization (Docker, Kubernetes, etc.)
- Fluency in spoken and written English is essential for effective communication.
- Work 40 hours a week in the US time zone area with 4 hours of overlapping time.
Desired skills
- Familiarity with machine-learning systems
- Comprehension of batch data processing and experience in building real-time analytic systems. Learn more about batch data processing.
- Hands-on experience with Golang and Scala
- Comprehension of highly distributed, scalable systems with minimal latency
- Expertise in data visualization and BI solutions such as Power BI, Tableau, and others. Read more about data visualization and BI solutions to understand their importance.
- Experience in REST API development
- Excellent organizational and communication skills
- Strong technical, analytical, and problem-solving skills
