Data Platform Engineers

Recruit Data Platform Engineers with Works

Data platform engineering is a complex and expansive field that focuses on designing and executing dependable and effective infrastructures to facilitate the continuous flow of data in a data-driven environment. These professionals are accountable for managing and integrating data from various sources to make sure that it is clean and ready to be used by other members of the organization to make enlightened data-driven decisions.

Data Platform Engineering includes creating, maintaining, and designing large-scale data collection, storage, and analysis systems. With many industries generating ever-increasing volumes of data, this field of engineering has become crucial. It is paramount to have the right technology and personnel in place to ensure that data scientists and analysts can access accurate, reliable, and meaningful data.

Data platform engineers are responsible for constructing and creating systems that can collect, analyze, and convert raw data into useful information for data scientists and business analysts. The primary objective of these systems is to make data more accessible to businesses, enabling them to better evaluate and improve their operations.

What is Involved in Data Platform Engineering?

Remote Data Platform Engineers are in high demand in the job market due to their indispensable skillset and expertise. Companies across several industries recognise the immense value these professionals bring to the table and are willing to offer them competitive salaries and other benefits.

As businesses seek data-driven insights, they require highly skilled engineers who specialise in data platforms. Data platform engineers work with vast and intricate datasets to provide insightful information for businesses, and their earning potential has grown accordingly. There is an increasing demand for individuals who have advanced knowledge of Big Data and who can effectively use data to achieve their business objectives.

What are the Responsibilities and Functions of Data Platform Engineers?

The key responsibility of a Data Platform Engineer is to establish and construct a robust infrastructure that enables Data Scientists to interpret data correctly. It is crucial for remote Data Platform Engineers to identify patterns in vast datasets and develop scalable algorithms that can transform semi-structured and unstructured data into useful representations. The engineer must convert and transform raw data into a form suitable for analytical or operational purposes. The following are some of the requisite duties and responsibilities associated with remote Data Platform Engineer positions:

• Establish a scalable data architecture that integrates data extraction and manipulation.
• Develop a comprehensive comprehension of data platform expenses to formulate strategic and cost-effective solutions.
• Establish data products and data streams to facilitate the continued growth of the data platform.
• Participate in data cleaning and data quality initiatives by constructing automated data pipelines for platform engineering.
• Create well-styled, documented, and verified high-performance code.
• Translate complex functional and technical requirements from intricate designs.
• Technologies like NoSQL, Hadoop, and others are used to store data.
• Create models to uncover concealed data patterns.
• Integrate data management strategies into the existing organisational structure.
• Third-party integration can be useful in building a strong infrastructure.
• Develop scalable and high-performing web services for monitoring data.

What are the ways to secure a job as a data platform engineer?

If you possess the appropriate combination of experience and skills, you can jumpstart or advance your career in data platform engineering. A Bachelor’s degree in Computer Science or a relevant field is typically a requirement for individuals seeking to work in this field. Such a degree establishes a foundation of knowledge that is vital to maintaining an edge in an ever-changing industry. Pursuing a Master’s degree can further advance your career and open doors to more lucrative positions.

Those specialising in data platforms usually have a background in computer science, engineering, applied mathematics, or another IT-related field. Individuals aspiring to become Data Platform Engineers may need more than just a boot camp or certification to acquire the technical expertise required to excel in the profession.

To qualify for remote Data Platform Engineering jobs, candidates must have a strong grasp of SQL database architecture and demonstrate programming proficiency in multiple languages, including Python and Java. Possessing Information Technology, Mathematics, or Analytics-related experience through boot camps, certifications or other relevant means, may support your candidacy for such positions.

If you lack prior experience in technology and information technology, it is highly recommended that you enrol in a comprehensive program to demonstrate your expertise. If you do not possess a bachelor’s degree, enrolling in an undergraduate degree program related to the field is advisable, or explore a master’s degree program in data analytics or data platform engineering if you already possess a bachelor’s degree outside the relevant field. By analysing job postings and identifying skills and qualifications desired by employers, you can gain insight into how your skills and experience can be applied to that role.

Key skills required for Data Platform Engineers

1. Spark and Hadoop

   Apache Hadoop is a robust software framework that allows distributed processing of large data sets across computer clusters. The framework scales seamlessly from a single server to thousands of nodes, each with its own computing and storage capabilities. Supported programming languages are Python, Scala, Java, and R. Despite being the most effective means of processing massive volumes of data, Hadoop has certain drawbacks, such as delayed processing and a higher level of coding complexity. Apache Spark, an alternative technology for handling large data sets, is a data processing engine that supports stream processing, which entails immediate data input and output. Though Spark and Hadoop have similarities, Spark is optimised for stream processing.

2. C++

   For fast calculation of large data sets without a predefined algorithm, C++ is an excellent choice. This programming language is capable of processing data sets of up to 1GB per second, making it a dependable and efficient solution. C++ can also be employed to retrain data in real-time while maintaining the system of record. (Learn more about C++’s role in processing large data sets here.)

3. Data Warehousing

   A data warehouse is an organised collection of relational databases used for long-term data storage and analysis. This method of storing data enables users to search and analyse information over time, gaining deeper insight into trends, patterns, and changes. In contrast, a database stores and organises data in real-time and is continuously updated. (Explore data warehousing technologies like Amazon Web Services and Amazon Redshift in more detail here.) Data platform engineers should have a comprehensive understanding of prevalent data warehousing technologies, as well as proficiency in utilising AWS given their AWS-reliant nature in remote positions.

4. Azure

   Azure is a Microsoft-developed cloud platform that enables data platform engineers to create and deploy powerful data analytics applications. The platform offers a comprehensive and user-friendly solution, including a variety of pre-built services, from data storage to intricate machine learning. As a result, Azure has gained significant popularity among data platform developers, with many opting to specialise in it.

5. NoSQL and SQL

   The Structured Query Language (SQL) is the widely used programming language for creating and managing relational databases, which consist of rows and columns. However, there are also non-tabular databases such as graphs and texts that employ NoSQL data format. Moreover, those who work with data platforms must be knowledgeable about Database Management Systems (DBMS), which are software programmes that interface with databases, enabling efficient storage and retrieval of information.

6. ETL (Extract, Transform, Load)

   ETL (Extract, Transform, Load) is a batch processing technique that involves collecting data from multiple sources, transforming it into a usable format, and storing it in a data warehouse. This process enables businesses to analyse and evaluate data related to specific business concerns. The ETL procedure involves acquiring data from diverse sources, applying business rules, transforming the data, and then storing it in a database or business intelligence platform accessible by all members of the organisation.

Where to search for remote Data Platform Engineer roles?

Working as a programmer can be highly satisfying. A comprehensive understanding of programming languages is paramount to succeeding in this role. Perfecting your craft through consistent practice is recommended for an excellent outcome. Additionally, having a clear product vision is essential to maintaining team alignment. Good communication skills facilitate effective collaboration with team members, as well as job prioritisation that aligns with long-term objectives.

At Works, we aim to simplify your search for the perfect remote Data platform engineering job. Our platform features only the best remote Data platform engineering jobs that can help elevate your Data platform engineering career. We provide access to a network of the world’s leading developers, enabling you to secure full-time, long-term remote Data platform engineering jobs with better pay and opportunities for growth.

Job Overview

Job Responsibilities

• Develop an adaptable data architecture that includes data extraction and transformation.
• Analyse the cost of data platforms to create strategic, cost-effective solutions.
• Design data flows and products that contribute to the continued expansion of the data platform.
• Produce code that is well-documented, verified, high-performing, and elegantly styled.
• Participate in activities focused on improving data quality and cleaning.
• Develop automated data engineering pipelines.

Requirements

• Bachelor’s or Master’s degree in Engineering, Computer Science (or equivalent experience)
• A minimum of three years of experience in data engineering is required (with some exceptions for exceptionally skilled developers).
• Ability to build real-time data streaming pipelines using Change Data Capture (CDC), Kafka, and Streamsets/NiFi/Flume/Flink
(Experience Preferred).
• Demonstrated proficiency in big data technologies such as Hadoop, Hive, and other related platforms.
• Familiarity with Change Data Capture tools such as IBM Infosphere, Oracle Golden Gate, Attunity, and Debezium.
• Hands-on experience in ETL technical design, automated data quality testing, documentation and quality assurance, data warehousing, data modelling, and data wrangling.
• Essential qualifications include a comprehensive understanding of Unix systems, experience in deploying applications on at least one major public cloud platform (such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure), and familiarity with DevOps automation tools like Terraform and Puppet.
• The ideal candidate is highly experienced with RDMS and NoSQL databases like MongoDB, ETL pipelines, Python, Java APIs that utilize spring boot, and complex SQL queries.
• Proficiency in backend programming using Python, Java, and other relevant technologies is essential.
• Proficiency in English is necessary for effective communication.
• This is a full-time position (40 hours per week) that requires availability for at least four hours overlapping with US time zones.

Desirable skills

• Familiarity with data systems or data pipelines (basic knowledge preferred).
• Knowledge of integrating machine learning models into production data pipelines.
• Proficiency in cloud-based data warehousing technologies, such as Snowflake, is required.
• Stay up-to-date with modern code development practices.
• Understanding of key AWS services and concepts, including S3, IAM, and Autoscaling Groups.
• Familiarity with DevOps concepts.
• Proficiency in SQL and relational database modelling techniques is required.
• Effective analytical, consultative, and communication skills are essential.