Remote Spark data engineer jobs

We, at Turing, are looking for talented remote Spark data engineers who will be responsible for cleaning, transforming, and analyzing vast amounts of raw data from various resources using Apache Spark to provide ready-to-use data to the developers and business analysts. Get a chance to work with the leading Silicon Valley companies while accelerating your career.

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Job description

Job responsibilities

  • Build and optimize Apache Spark ETL pipelines
  • Deliver scalable, cost-effective and flexible solutions to clients
  • Participate in iterative, end-to-end application development
  • Keep up with modern software development best practices and lifecycle management
  • Use ETL tools to load data from different sources into the Hadoop platform
  • Communicate regularly in an efficient manner with customers and stakeholders
  • Create Java-based Spark jobs for data transformations and aggregations
  • Conduct units tests for Spark transformations
  • Implement data processing pipelines with Spark

Minimum requirements

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • At least 3+ years of experience data engineering (rare exceptions for highly skilled developers)
  • Expertise in established programming languages like Python, Java, Scala, etc.
  • Mastery of Apache Spark and different Spark Frameworks/Cloud Services like Databricks, EMR, Azure HDI
  • Experience with technologies such as Storm, Apache Kafka, Hadoop, etc.
  • In-depth knowledge of Cloud (AWS, Azure) as well as CI/CD and data visualization
  • Practical experience with containerization technologies and container orchestration using Kubernetes, OpenShift, Docker, etc.
  • Knowledge of technologies like Spark and Hadoop HDFS, Hive, Hbase with deep expertise in Spark
  • Fluency in English language for effective communication
  • Ability to work full-time (40 hours/week) with a 4 hour overlap with US time zones

Preferred skills

  • Comfortable with ETL concepts, SQL (DDL, DML, procedural)
  • Hands-on experience on change capture and ingestion tools like StreamSets, Informatica
  • Strong experience in source code repositories like Git, SVN and Jenkins
  • Working knowledge of NRT and associated tech stack - Spark, MemSQL, etc.
  • Understands data architecture, data profiling, and data quality
  • Knowledge of data warehouse databases like Teradata, Oracle, etc.
  • Familiarity with Unix and Shell Scripting
  • Knowledge of diverse industry and tools and data warehousing technologies
  • Hands-on experience to build and administer VMs and containers
  • Working knowledge on HashiCorp Vault Consul is desirable
  • Excellent communication and organizational skills
  • Professional Certificates in AWS, RHCE, DevOps will be a plus

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How to become a remote Spark data engineer?

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.

What is the scope of a Spark data engineer?

With an increasing demand for big data solutions and related technologies, Spark data engineers seem to have a prosperous future. The use of Spark as technology has significantly increased over the years and across different industries as developers are using the framework with different languages. Spark offers support for various programming languages like Scala, Python, and Java allowing developers to adopt an agile approach. A major part of the software development industry has already adopted Spark as a preferred choice, with many more joining the fray.

Most top organizations around the world are investing heavily to build a proven Spark talent cloud. This has transformed Spark into a high-value skill based on which developers can take their career to the next. Any developer with some years of professional experience and expertise in Spark and its best practices can easily build a high-paying and successful career. Spark developers are not just in-demand professionals within the tech community but rather across industries. Spark is globally used and regularly deployed in industries like telecommunication, networking, banking and finance, retail, software development, media, and entertainment, consulting, healthcare, manufacturing, and more.

The ability to find success in different industries and work with large corporations have made Spark data engineering more lucrative than ever. Developers around the world try to secure the best opportunities in the domain with countless companies seeking Spark experts at the same time. A sustained demand to find top Spark specialist among software development companies has made it a prosperous career path.

What are the responsibilities and roles of a Spark data engineer?

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

How to become a Spark data engineer?

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.

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Skills required to become a Spark data engineer

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.

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How to get hired as a remote full-stack Spark data engineer?

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.

Why become a Spark data engineer at Turing?

Elite US jobs
Elite US jobs

Long-term opportunities to work for amazing, mission-driven US companies with great compensation.

Career growth
Career growth

Work on challenging technical and business problems using cutting-edge technology to accelerate your career growth.

Exclusive developer community
Exclusive developer community

Join a worldwide community of elite software developers.

Once you join Turing, you’ll never have to apply for another job.
Once you join Turing, you’ll never have to apply for another job.

Turing's commitments are long-term and full-time. As one project draws to a close, our team gets to work identifying the next one for you in a matter of weeks.

Work from the comfort of your home
Work from the comfort of your home

Turing allows you to work according to your convenience. We have flexible working hours and you can work for top US firms from the comfort of your home.

Great compensation
Great compensation

Working with top US corporations, Turing developers make more than the standard market pay in most nations.

How much does Turing pay their Spark data engineer?

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.

Frequently Asked Questions

We are a Palo Alto-based 'deep' jobs platform allowing talented software developers to work with top US firms from the comfort of their homes. We are led by Stanford alumni and successful A.I. entrepreneurs Jonathan Siddharth and Vijay Krishnan.

After uploading your resume, you will have to go through the three tests -- seniority assessment, tech stack test, and live coding challenge. Once you clear these tests, you are eligible to apply to a wide range of jobs available based on your skills.

No, you don't need to pay any taxes in the U.S. However, you might need to pay taxes according to your country’s tax laws. Also, your bank might charge you a small amount as a transaction fee.

We, at Turing, hire remote developers for over 100 skills like React/Node, Python, Angular, Swift, React Native, Android, Java, Rails, Golang, PHP, Vue, among several others. We also hire engineers based on tech roles and seniority.

Communication is crucial for success while working with American clients. We prefer candidates with a B1 level of English i.e. those who have the necessary fluency to communicate without effort with our clients and native speakers.

Currently, we have openings only for the developers because of the volume of job demands from our clients. But in the future, we might expand to other roles too. Do check out our careers page periodically to see if we could offer a position that suits your skills and experience.

It is the combination of our core business model and values that makes us different from others. We provide full-time, long-term projects to remote developers whereas most of our competitors offer more freelance jobs.

No, the service is absolutely free for software developers who sign up.

Ideally, a remote developer needs to have at least 3 years of relevant experience to get hired by Turing, but at the same time, we don't say no to exceptional developers. Take our test to find out if we could offer something exciting for you.

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In a nutshell, Turing aims to make the world flat for opportunity. Turing is the brainchild of serial A.I. entrepreneurs Jonathan and Vijay, whose previous successfully-acquired AI firm was powered by exceptional remote talent. Also part of Turing’s band of innovators are high-profile investors, such as Facebook's first CTO (Adam D'Angelo), executives from Google, Amazon, Twitter, and Foundation Capital.

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