Data Platform Engineers

Hire Data Platform Engineers

Data platform engineering is a large subject that includes a variety of titles, with a core emphasis on building reliable infrastructures that allow for continuous data flow in a data-driven environment. These professionals act as conduits for clean and raw data from many sources, enabling workers to utilize it to make data-driven choices inside the company.

Data platform engineering is the process of developing and implementing large-scale data collection, storage, and analysis systems. It is a vast area with applications in almost every sector. Organizations may gather vast volumes of data, but they need the proper people and technology to guarantee that data scientists and analysts get meaningful data.

Data platform engineers design and build systems that gather, analyze, and turn raw data into information that data scientists and business analysts may use in a range of settings. The ultimate objective is to make data more available to businesses, allowing them to assess and improve their performance.

What does data platform engineering entail?

A remote Data platform engineer is one of the most in-demand careers in the market. Businesses in all areas consider them highly, and they are well compensated for their efforts.

As more businesses get on the Big Data bandwagon and mine data for meaningful insights, the need for data-related employment continues to rise. This guideline applies to engineers who deal with data. Companies are always looking for competent Data platform engineers that can work with big amounts of complicated data to give meaningful business insights. The revenue potential of data platform engineers has also increased as a consequence of the job needing a high degree of Big Data expertise and skill.

What are the duties and functions of data platform engineers?

A Data platform engineer’s primary responsibility is to design and build a dependable infrastructure for turning data into forms that Data Scientists can understand. Remote Data platform developers must be able to spot patterns in massive datasets in addition to developing scalable algorithms to convert semi-structured and unstructured data into meaningful representations. Data platform engineers process and convert raw data so that it may be utilized for analytical or operational purposes. Let us now look at the responsibilities of remote Data platform engineer jobs:

  • 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 organizational 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?

You may start or enhance your career in data platform engineering if you have the correct mix of skills and experience. A bachelor’s degree in computer science or a similar subject is often held by data platform engineers. A degree may help you establish a firm foundation of knowledge in an ever-changing sector. A master’s degree may also help you progress your career and get access to higher-paying jobs.

Engineers that work on data platforms are often trained in computer science, engineering, applied mathematics, or a similar IT subject. Prospective Data platform engineers may discover that a boot camp or certification is inadequate since the profession needs a high level of technical understanding.

You must be familiar with SQL database architecture and have programming skills in a variety of languages, including Python and Java. A boot camp or certification may help you create a CV for remote Data platform engineering jobs if you already have a background in IT or a related field such as mathematics or analytics.

If you have no past expertise with technology or IT, you may need to take a more intensive program to show your comprehension. If you don’t already have one, you may want to consider enrolling in an undergraduate degree. If you have a bachelor’s degree but it isn’t in a relevant field, look into master’s degrees in data analytics and data platform engineering.

If you spend some time looking through job advertisements to see what companies are looking for, you’ll have a better idea of how your expertise fits into that function.

Data platform engineers must have certain skills

  1. Spark and Hadoop

    The Apache Hadoop software library is a framework that uses basic programming concepts to allow the distributed processing of massive data volumes across clusters of devices. It is intended to grow from a single server to tens of thousands of devices, each with its own processing and storage capacity. Python, Scala, Java, and R are among the programming languages supported by the framework. While Hadoop is the most powerful technology for handling enormous amounts of data, it does have certain downsides, such as delayed processing and a high degree of coding. Apache Spark is a data processing engine that allows stream processing, or real-time data input and output. It is similar to Hadoop in that it performs many of the same functions.
  2. C++

    When you don’t have a predefined algorithm, C++ is a very simple but powerful programming language for swiftly calculating large data sets. It is the only programming language that can handle over 1GB of data in a single second. You may also use real-time predictive analytics to retrain the data while maintaining the system of record.
  3. Warehousing of Data

    A data warehouse is a relational database where information can be searched and examined. Its goal is to present you with a long-term view of data throughout time. A database, on the other hand, regularly updates real-time data. Data platform engineers must be well-versed in the most popular data warehousing technologies, such as Amazon Web Services and Amazon Redshift. AWS is required for almost all remote Data platform engineer positions.
  4. Azure

    Azure is a Microsoft cloud platform that allows data platform engineers to create large-scale data analytics applications. It provides a simple-to-deploy comprehensive analytics solution for supporting apps and servers. The bundle contains pre-built services for everything from data storage to complex machine learning. Due to the popularity of Azure, several data platform developers have opted to specialize on it.
  5. NoSQL and SQL

    The SQL programming language is the industry standard for developing and maintaining relational database systems (tables that consist of rows and columns). Non-tabular NoSQL databases occur in a number of shapes and sizes depending on their data format, such as a graph or a text. Database management systems (DBMS), which are software programs that offer an interface to databases for information storage and retrieval, must be known to data platform developers.
  6. ETL (Extract, Transfer, Load)

    ETL refers to the process of extracting data from a source, converting it into a usable format, and storing it in a data warehouse (Extract, Transfer, Load). This method employs batch processing to assist users in evaluating data relevant to a given business issue. The ETL collects data from diverse sources, applies business rules to it, and then stores the transformed data in a database or business intelligence platform that everyone in the company can access and utilize.

Where can I find remote Data platform engineer jobs?

Working as a programmer may be quite rewarding. However, a strong understanding of programming languages is essential. It is recommended that you practice until you reach perfection. Furthermore, having a product vision is required for staying on track with the team. Good communication skills facilitate collaboration with team members and job prioritization based on long-term goals.

Works has made your search for remote Data platform engineering jobs a little simpler. Works features the best remote Data platform engineer jobs that can help you progress in your Data platform engineer career. Join a network of the world’s best developers to get full-time, long-term remote Data platform engineer jobs with higher pay and opportunities for advancement.

Job Description

Responsibilities at work

  • Create a scalable data architecture that includes data extraction and transformation.
  • Create cost-effective and strategic solutions by analyzing 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.


  • 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 modeling, and data wrangling.
  • Knowledge with Unix and DevOps automation tools like as Terraform and Puppet, as well as expertise deploying apps to at least one major public cloud provider such as AWS, GCP, or Azure.
  • Extensive familiarity with RDMS and a NoSQL database such as MongoDB, ETL pipelines, Python, Java APIs utilizing 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 modeling techniques.
  • Excellent analytical, consultative, and communication abilities.