Remote Spark data engineer jobs

Spark or Apache Spark is a general-purpose data processing engine used by developers globally. It seems to be the perfect solution for handling the business requirements of various industries and circumstances. The Spark core data processing engine comes with libraries for SQL, ML, graph computing, and stream processing features which add to its list of advantages. Spark is not just utilized by app developers but also globally used by data scientists to configure quick queries, analyze, and transform data at scale.

Spark is also well known for being a preferred solution frequently associated with processing large datasets, streaming data from sensors, IoT, financial systems, and ML tasks. Over the years Spark has managed to become a go-to choice for a majority of the developers transforming it into a high-value skill. It has not only managed to streamline several processes but also given organizations available alternatives for developing fast-scaling applications to meet evolving end-user preferences. For which, tech firms around the world are always looking to hire Spark data engineers capable of driving projects and addressing business requirements using technology.

As a Spark data engineer, you should be prepared to contribute to different aspects of software development processes. When hired as a Spark data engineer, some of your daily responsibilities should include tasks like developing applications utilizing modern languages like Scala, Python, and Java. You also need to work closely on developing Spark tests for data aggregation and transformation. You also need to be able to design different data processing pipelines and conduct peer code reviews to ensure the quality of scripted logic. As a Spark data engineer, you should also be prepared to undertake tasks like gathering intel on user preferences and transforming them into robust features for new and exciting applications. So, while working as a Spark data engineer, expect to take ownership of tasks like:

• Develop and optimize Apache Spark ETL pipelines
• Produce easy to upgrade, cost-effective, and flexible solutions for clients
• Actively contribute to end-to-end application development processes
• Stay updated about modern software development best practices and management
• Utilize ETL tools for accessing data from various sources into a Hadoop platform
• Collaborate efficiently with different customers and stakeholders
• Craft Java-based Spark jobs for data transformations and aggregations
• Perform units tests for Spark transformations
• Configure data processing pipelines using Spark

Knowledge of Spark development and data engineering is an extremely high-value skill in the present software industry. The technology has been in use for around a decade and can help developers build careers specializing on it. In order to find success in such roles, developers must possess a thorough understanding of certain basic skills. Companies prefer to hire Spark data engineers with relevant professional experience and a deep understanding of Apache Spark and different Spark Frameworks and cloud services. The ability to work using technologies like Storm, Apache Kafka, or Hadoop should also help to secure the best opportunities at top companies. As a developer, try to master different technologies and approaches adopted by Spark data engineers for developing large-scale projects.

In addition to technical proficiency, most organizations prefer to hire developers with a degree in Computer Science or related fields. Furthermore, always try to stay updated about the latest developments in the field Spark development and related processes.

If you wish to build a long-term successful career in software development as a Spark data engineer, you need to possess a certain set of expertise. Try to build up a deep understanding of technologies and languages including:

1. Apache Spark

Apache Spark is a free-to-use unified analytics engine often utilized for large-scale data processing. It offers an intuitive interface for configuring clusters with implicit data parallelism and fault tolerance. The platform utilizes in-memory caching, and optimized query execution for prompt queries about data of a variety of sizes. Using Spark, developers can build APIs in several languages like Java, Scala, Python, and R. Spark is also preferred as offers code reusability features for different processes like batch processing, interactive queries, real-time analytics, machine learning, and graph processing. As a development platform, Apache Spark is extremely fast, efficient, developer-friendly, and supports multiple workloads.

2. Python

Another essential skill required to work as a Spark data engineer is Python. It is probably the most widely used general-purpose programming language today. Initially developed to offer code readability and indentations, Python quickly carved out its niche and a global following. Python as a language was built with an object-oriented approach to allow programmers to write clean and logical codes for various industries and requirements. The language can be used for developing digital solutions for different industries and sees a constant spotlight in sectors like - data analytics, machine learning, and other data-driven projects. It is also an extremely versatile language and offers support for essential tasks that can define a project’s success.

3. AWS/Microsoft Azure

In the current software development industry, almost every new product utilizes cloud services in some manner. Cloud services have managed to introduce several benefits for developers to build, scale, and manage projects with minimum effort and from any location. The introduction of such technology has managed to streamline different processes making it a vital requirement for almost every software development role. Tech firms mostly for skilled Spark data engineers with a thorough knowledge of cloud integrations and development best practices. Such services have also revolutionized how development strategies are devised. Based on the benefits of cloud services, most companies seek expertise in AWS or Azure development while hiring SPark data engineers.

4. Containerization

Containerization has fast become a frequently opted model by software developers. It offers a type of virtualization technology that allows applications to run on their individual/isolated spaces referred to as containers. In the present software development industry, almost every software development project incorporates container-based models to utilize servers with continuous efficiency. Most tech firms actively try to source experts with a proven skill set to build, configure, and maintain containerized projects. As a Spark data engineer, a thorough understanding of technologies, Docker, and Kubernetes should be treated as a top priority to build a successful and steady career.

5. Versioning tools

Modern software development processes mostly utilize small modules of code to improve stability. Developers also prefer working with the same model as it enables them to add, modify, or disable certain features without having to disrupt the entire sourcecode. Such benefits have transformed versioning tools to a position of importance. Using such tools developers can keep a track of the entire code base during and even after the release of applications. This allows developers to not just monitor and find areas for improvement, but also switch back to a stable version of the program if or whenever required. For which, understanding and professional experience of working with version control systems have become a essential skill to build a successful career in the modern software development industry.

6. Communication skills

To work in the modern software development industries, developers might need a lot more than just technical proficiency. In the current industry, companies prefer to hire technical wizards with the confidence of interacting and presenting to various tea members. The ability to communicate with efficiency is not just good to have but rather a mandatory requirement for most positions. Spark data engineers need to possess confidence in their skills and fluency in preferred languages to contribute effectively to development processes. Interacting and communicating with various teams and stakeholders sound to be a daily responsibility for most developers. Interpersonal skills have become even more important with remote positions becoming increasingly popular. For which every Spark data engineer needs to be a confident communicator.

Top tech organizations look to hire Spark data engineer with experience of working on various niches. For which, constantly building up technical skillset and gathering knowledge about requirements of various industries is a must. Along with knowledge of Spark data engineer, developers are also expected to be well-versed in working with related technologies and possess efficient interperdsonal skills. Developers with an understanding of user preferences also tend to be a better prospect for organizations.

Turing has quickly become a premier platform for taking careers forward working as a remote Spark data engineer. We provide developers opportunities to work on era-defining projects and business problems using state of the art technologies. Join the fastest growing network of the top developers around the globe to get hired as a full-time and long-term remote Spark data engineer with the best pay packages.

Every Spark data engineer at Turing can set their own pricing. Turing, on the other hand, will recommend a salary to the Spark data engineer for which we are confident of finding a fruitful and long-term opportunity for you. Our salary recommendations are based on an analysis of market conditions as well as customer demand.