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The Client:

Our customer is Smart Fusion Solutions. The company helps businesses with IT teams augmentation of different engineers in e-commerce, online gaming, and financial


services platforms. Smart Fusion Solutions needed help to build a Data Engineering department for its clients: a large e-commerce enterprise and online gaming platform. Our customer has no previous experience in Data Engineering technologies, so they contacted us to help with this expertise.

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A Case Study Augmenting the Data Engineering Team to Build a Reliable Data Platform

The Business and Technical Challenges:

  1. To help our client to build a data engineering team

  2. To help build the platform, architect the entire data platform and system, which grew into a large e-commerce project with data scraping and analytics


The Solution: 

We provided our Data Engineer with a Ph.D. degree and Data Quality Engineer to build a strong team for our client that was able to help build the platform.

We also involved a data architect to design the entire data platform and system. As a result, it grew into a large Data project for e-commerce with robust Data Pipelines, including data scraping and data preparation for BI tools for analysis.


The Result:

Our engineers helped with the following:

  • started Data Engineering team for both clients

  • helped to build the data architecture of the system that scales

  • helped with many Data Engineering tasks like ETLs

  • assisted with data quality to ensure it’s consistent and according to the requirements

  • consulting regarding data architecture, infrastructure, and data systems best practices

  • build data pipelines

  • preparing the data for BI tools

  • third-party data sources integrations

  • help to work with data scraping tools


Smart Fusion Solutions was able to successfully build a Data Engineering department for its clients, despite having no experience in Data Engineering technologies. Our engineers were able to help the clients build a scalable data architecture and system, as well as perform a variety of Data Engineering tasks. The clients were able to improve their data quality and gain insights from their data, thanks to the help of our engineers.


The Tech Stack, Used in the Project:

  • Amazon AWS Redshift, Snowflake for Data Warehousing 

  • AWS EMR as the core data platform

  • Apache Spark with Scala for data processing

  • Kubernetes as one of the core infrastructure

  • Python for scripting and data processing

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The Data Security:

Our engineers are Data Analytics Certified and follow the best practices when building Data Systems - AWS safety rules, as well as VPN, Private/Public subnets, production and development environments separated in different AWS Accounts, and AWS Secrets Manager.

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        Our Clients Say:

Yaroslav Kuntsevych
"DataEngi is our partner when our customers need expertise in data. There aren’t many teams with experience in Scala, and DataEngi expertise was in Scala and Python. Additionally, some of their team-taught at the university and have a mathematical background. DataEngi’s team has good expertise in data engineering, the cloud, and backend technology. Bugs being resolved by DataEngi were critically impacting app performance. DataEngi offers a deep, experienced talent pool and prides themselves on providing friendly customer service."

Yaroslav Kuntsevych, Director, Smart Fusion Solutions, Inc.

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