Data Platform Engineers

Hire Data Platform Engineers

Data platform engineering is a broad and complex field, focusing mainly on the design and implementation of efficient and reliable infrastructures for the continuous flow of data in a data-driven environment. These professionals are responsible for the management and integration of data from numerous sources in order to ensure that it is clean and ready to be used by other members of the organisation to make data-driven decisions.

Data Platform Engineering encompasses the design, creation and maintenance of large-scale data collection, storage and analysis systems. This field of engineering has a wide range of applications in many industries, and is becoming increasingly important as organisations generate ever-increasing volumes of data. Having the right personnel and technology in place is critical to ensure that data scientists and analysts have access to accurate, reliable and meaningful data.

Data platform engineers are responsible for creating and constructing systems that are capable of collecting, analysing, and transforming raw data into useful information for data scientists and business analysts. The main purpose of these systems is to make data more accessible to businesses, thereby allowing them to better evaluate and enhance their operations.

What does data platform engineering entail?

As a Remote Data Platform Engineer, there is an increasing demand in the job market due to the invaluable skillset and expertise they possess. Companies from a variety of industries recognise the immense value these professionals bring to the table, and they are willing to reward them with competitive salaries and other benefits.

As the demand for data-driven insights increases, businesses are looking for highly-skilled engineers that specialise in data platforms. Data platform engineers are tasked with working with large and complex datasets to provide meaningful business insights, and their revenue potential has grown accordingly. Companies are increasingly in need of individuals with an advanced knowledge of Big Data who can effectively utilise data to maximise their business objectives.

What are the duties and functions of data platform engineers?

As a Data Platform Engineer, the primary responsibility is to design and build a robust infrastructure that allows Data Scientists to interpret data correctly. It is essential for remote Data Platform Engineers to be able to identify patterns in large datasets and to develop scalable algorithms that can convert semi-structured and unstructured data into useful representations. The engineer also needs to transform and convert raw data into a form that can be used for analytical or operational purposes. The following are some of the duties and responsibilities of remote Data Platform Engineer positions:

• Create a data architecture that is scalable and incorporates data extraction and manipulation.
• Build a full understanding of data platform expenses in order to develop cost-effective and strategic solutions.
• Create data products and data flows to help the data platform’s further expansion.
• Take part in data cleaning and data quality initiatives – Create automated data platform engineering pipelines.
• Write high-performance code that is well-styled, verified, and documented.
• Complicated functional and technical needs must be translated from sophisticated designs.
• Data is stored using Hadoop, NoSQL, and other technologies.
• Make models to discover hidden data patterns.
• Data management strategies must be incorporated into the current organisational structure.
• Third-party integration may help with the construction of a solid infrastructure.
• Create high-performance, scalable web services to monitor data.

How can I get a job as a data platform engineer?

If you possess the right combination of skills and experience, then you could start or boost your career in data platform engineering. A Bachelor’s degree in Computer Science, or a related field, is often considered a prerequisite for people looking to work as data platform engineers. Such a degree will provide you with the necessary base of knowledge to stay ahead of the game in a continuously evolving industry. Moreover, having a Master’s degree could help you to further your career and gain access to more lucrative positions.

Engineers that specialise in data platforms typically have a background in computer science, engineering, applied mathematics, or some other related IT field. Those interested in becoming Data Platform Engineers may find that taking a boot camp or receiving an accreditation is not sufficient to equip them with the necessary level of technical expertise required for the profession.

Applicants for remote Data Platform Engineering jobs must possess a strong working knowledge of SQL database architecture and demonstrate programming proficiency in several languages, including both Python and Java. Completion of a boot camp, an accreditation, or other relevant experience in Information Technology, Mathematics, or Analytics may be beneficial in creating a resume for such positions.

If you lack prior experience in the realm of technology and information technology, it is highly recommended that you enrol in a more comprehensive program to demonstrate your understanding. If you do not already possess a bachelor’s degree, consider enrolling in an undergraduate degree program that is related to the field. Alternatively, if you already have a bachelor’s degree but it is not in a relevant subject, you may want to explore the option of enrolling in a master’s degree program in data analytics or data platform engineering.

If you take the time to analyse job postings and determine what qualifications employers are seeking, you can gain a better understanding of how your skillset and experience can be utilised in that role.

Data platform engineers must have certain skills

1. Spark and Hadoop

   The Apache Hadoop software library is a powerful framework that enables distributed processing of large data sets across clusters of computers. The library is designed to scale up from a single server to tens of thousands of nodes, each with its own computing and storage capabilities. Supported programming languages include Python, Scala, Java, and R. While Hadoop is the most effective way to deal with massive volumes of data, it does come with some drawbacks, such as delayed processing and a higher complexity of coding. An alternative technology for handling substantial data sets is Apache Spark, a data processing engine that facilitates stream processing, or immediate data input and output. While Apache Spark and Hadoop are similar in certain aspects, Spark is optimised for stream processing.

2. C++

   When it comes to quickly calculating large data sets without a predefined algorithm, C++ is an ideal choice. This powerful programming language can handle data sets of up to 1GB per second, making it a reliable and efficient solution. Additionally, C++ can be used to retrain data in real-time, while also maintaining the system of record.

3. Warehousing of Data

   A data warehouse is an organised collection of relational databases that are used for the purpose of long-term data storage and analysis. This type of data storage allows users to search and review information over a period of time and gain a greater insight into trends, patterns, and changes. In contrast, a database is a system that stores and organises data in real-time and is constantly updated. As a result, data platform engineers must have a thorough understanding of the most popular data warehousing technologies, such as Amazon Web Services and Amazon Redshift. Furthermore, because these remote positions typically require the use of AWS, these engineers must be proficient in using this platform.

4. Azure

   Azure is a cloud platform developed by Microsoft that enables data platform engineers to build and deploy powerful data analytics applications. The platform provides a comprehensive and straightforward solution that includes a range of pre-built services, from data storage to complex machine learning. This has resulted in Azure becoming increasingly popular amongst data platform developers, many of whom have chosen to specialise in it.

5. NoSQL and SQL

   The Structured Query Language (SQL) is the go-to programming language for creating and managing relational databases (databases that consist of rows and columns). However, there are also non-tabular NoSQL databases that come in various formats such as graphs and texts. Additionally, those working with data platforms must be familiar with Database Management Systems (DBMS), which are software programs that provide an interface for storing and retrieving information from the databases.

6. ETL (Extract, Transfer, Load)

   ETL (Extract, Transfer, Load) is a process used to gather data from multiple sources, transform it into a usable format, and then store it in a data warehouse. This method of batch processing enables businesses to analyse and assess data related to specific business-related issues. The ETL process collects data from various sources, applies business rules to it, and then stores the transformed data in a database or business intelligence platform that can be accessed and utilised by all members of the organisation.

Where can I find remote Data platform engineer jobs?

Working as a programmer can be incredibly rewarding. To be successful in this role, it is essential to have a thorough understanding of programming languages. To ensure excellence in your work, it is recommended that you practice and strive for perfection. Additionally, having a comprehensive product vision is necessary for staying in sync with the team. Good communication skills enable effective collaboration with team members and enable job prioritisation that is aligned with long-term objectives.

At Works, we are committed to making your search for the ideal remote Data platform engineering job easier. We feature only the best remote Data platform engineering jobs that can help you take your career in Data platform engineering to the next level. With our platform, you will be joining a network of the world’s top developers, allowing you to secure full-time, long-term remote Data platform engineering jobs with higher pay and opportunities for career progression.

Job Description

Responsibilities at work

• Create a scalable data architecture that includes data extraction and transformation.
• Create cost-effective and strategic solutions by analysing the cost of data platforms.
• Create data products and data flows for the data platform’s ongoing growth.
• Write code that is high-performing, well-styled, verified, and documented.
• Take part in data cleaning and quality activities.
• Create data engineering pipelines that are automated.

Requirements

• Engineering or computer science bachelor’s/degree master’s (or equivalent experience)
• At least three years of expertise in data engineering is required (rare exceptions for highly skilled developers)
• Experience building real-time data streaming pipelines using Change Data Capture (CDC), Kafka, and Streamsets/NiFi/Flume/Flink.
• Proficient in large data technologies such as Hadoop, Hive, and others.
• Knowledge of Change Data Capture tools such as IBM Infosphere, Oracle Golden Gate, Attunity, and Debezium.
• ETL technical design experience, automated data quality testing, QA and documentation, data warehousing, data modelling, and data wrangling.
• Having a thorough understanding of Unix systems, DevOps automation tools (e.g. Terraform and Puppet), and experience in deploying applications to at least one major public cloud platform (for example, Amazon Web Services, Google Cloud Platform, or Microsoft Azure) is essential.
• Extensive familiarity with RDMS and a NoSQL database such as MongoDB, ETL pipelines, Python, Java APIs utilising spring boot, and complicated SQLs.
• Solid Python, Java, and other backend programming abilities are required.
• English fluency is required for good communication.
• Work full-time (40 hours a week) with a 4-hour overlap with US time zones.

Preferred skills

• Basic knowledge of data systems or data pipelines.
• Understanding of how to integrate learned ML models into production data pipelines.
• A solid grasp of cloud warehousing technologies such as Snowflake.
• Learn about current code development methods.
• Knowledge of fundamental AWS services and concepts (S3, IAM, autoscaling groups)
• Basic understanding of DevOps.
• SQL capabilities and knowledge of relational database modelling techniques.
• Excellent analytical, consultative, and communication abilities.