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Join our Data and Analytics Practice in Hungary

Through the power of data, we help our clients thrive in a market challenged by the pressures of digitization. Our team is growing quickly in Hungary and we invite you to join us.

Our Expertise

SENIOR DATA ENGINEERS
LEAD DATA ENGINEERS
DATA SOLUTION ARCHITECTS

TECHNICAL CONSULTANTS
DATA SCIENTISTS
DELIVERY MANAGERS

ABOUT EPAM

275+
Forbes Global 2000 Customers


47,850+*
Total EPAMers

* Data for Q2 2021

35+
Countries


FORTUNE

Ranked as the top IT services company on Fortune's 100 Fastest-Growing Companies List in 2019 and 2020

OPEN SOURCE

3,000+ contributors and 140,000+ commits in the public domain. Read more.

Why EPAM

  • Possibility to work with start-ups and enterprise organizations.
  • Gain access to a variety of projects, domains and technologies.
  • Contribute to innovative and impactful projects.
  • Develop new skills, find mentors and connect with global colleagues who possess deep technical expertise.
  • Unlimited opportunities for career and professional growth.
  • Receive opportunities for tuition, reimbursement, industry training and domain certification.
  • The Hungarian practice is dynamically growing. We are actively building the data community within and outside of EPAM via organizing conferences, meetups, blogging and project showcases.

ABOUT THE HUNGARIAN DATA PRACTICE TEAM

  • Our Data Practice is growing at an accelerated pace in Hungary
  • We deliver unique cloud-based, distributed data solutions for problems that conventional database technologies cannot solve.
  • We are committed to developing internationally recognized experts in the data domain by providing unique upskilling trainings on high-demand skillsets and a list of technologies. 
  • We are working on international data architecture, cloud data management, data governance projects.
  • Through our long-term partnerships, we access multiple Petabyte sized clusters, some amongst the Top 25 largest data lakes in the world. 

“EPAM provides extensive career development paths, alongside architecture, pre-sales, consulting and mentoring opportunities. Whether you are interested in the rapid development of a full makeover of a data architecture or a long-term product development covering all product lifecycles, including operation and maintenance, we will find the best fit project for you.” 

Peter Kortvelyesi

Head of Data Practice in Hungary

“At EPAM we are working with Fortune 500 companies. We deliver the best possible solutions, we are working with modern, vendor-agnostic IT architectures. This, combined with our complex learning programs, allow unlimited professional growth for our people.” 

Andor Herendi

Software Engineering Manager, Data Practice, Budapest

“What I think is special about this position is that you need to work closely together with your clients, understand their problems and put them into a system that helps them interpret the data and make a decision accordingly. It is very interesting to see how data analysis, a BI report or a dashboard may visualize a problem to our client and make it more understandable.” 

Boglarka Birinyi

Lead Data Analyst, BI Solution Manager

Our Data & Analytics Practice

DATA ENGINEERING

Data engineers deal with problems that common database and data warehousing technologies would not be able to solve, be it streaming solutions, special data formats or high volume or velocity, complex data systems. 

DEVOPS IN DATA

System engineers integrate new functionality into projects, automate and fine-tune the release cycle, and design and develop data platforms. In addition to working with standard cloud services and Ansible/Jenkins/Bash, data DevOps engineers also work with Hadoop, NoSQL and correspondent migrations.

DATA QUALITY

Data quality engineers check system data for compliance with business requirements, usability and satisfying established quality metrics. They also build automated processes for data quality, checking on different architecture levels and processing stages.

DATA SCIENCE

Data scientists develop mathematical models and algorithms to allow the mining of business insights from data and automate cognitive business processes. They work with a wide range of techniques, including classical machine learning methods, neural networks and reinforcement learning.

BUSINESS INTELLIGENCE

BI engineers help clients build systems for analyzing KPIs and other important business parameters. The process comprises three stages: warehousing (developing analytical data storage), ETL (developing processes for extracting information from various sources, information refinement and transforming it into required format) and reporting (visualization and presenting graphic information based on table data).

BUSINESS ANALYSIS

Business analysts work with a wide range of business- or system-oriented tasks depending on project size and specific goals. Business-oriented tasks are intended to elicit customers’ requirements, while system-oriented ones are intended to analyze the information, develop data model, or even high-level system design sometimes.

DATA DELIVERY

Delivery managers ensure successful end-to-end delivery for complex programs, projects or multiple workstreams in the Data and Analytics Domain. The Data Delivery Manager is accountable for the overall project delivery, client satisfaction, and management of expectations while ensuring that the work is on strategy, on time, and withing budget.)

 

TECH STACK

TECH STACK

RECENT GLOBAL PROJECTS

Vehicle Condition Monitoring

This project’s goal was to monitor a car fleet in order to make predictions about which cars should be sent for inspection and which should be sold when repairs cost more than expected profit. A platform based on Snowflake was developed. ETL was built on Talend and Spark, with visualization on SAP BO.

Automating Experts’ Work for a Large Online Auction House

The customer for this project is one of the largest online auction houses in the world which specializes in the sale of rare and collectible items. They have a team of experts assessing lots in various categories. Our project involved automating the experts' work using machine learning algorithms. Using the accumulated data, we automated as much of the routine work as possible, enabling the experts to focus on the rarest and most specific lots. 

Recognizing Customer Actions in a Global Network of Health and Beauty Stores

Our client for this project was a health and beauty store chain operating in more than 25 countries. The project developed a system for recognizing and tracking customer actions in stores using surveillance cameras which are used by analysts to optimize store operations. Our product was a mixture of deep convolutional networks for detection and classical algorithms for object tracking. The technology stack was based on Python and TensorFlow.

RECENT GLOBAL PROJECTS

Vehicle Condition Monitoring

This project’s goal was to monitor a car fleet in order to make predictions about which cars should be sent for inspection and which should be sold when repairs cost more than expected profit. A platform based on Snowflake was developed. ETL was built on Talend and Spark, with visualization on SAP BO.

Automating Experts’ Work for a Large Online Auction House

The customer for this project is one of the largest online auction houses in the world which specializes in the sale of rare and collectible items. They have a team of experts assessing lots in various categories. Our project involved automating the experts' work using machine learning algorithms. Using the accumulated data, we automated as much of the routine work as possible, enabling the experts to focus on the rarest and most specific lots. 

Recognizing Customer Actions in a Global Network of Health and Beauty Stores

Our client for this project was a health and beauty store chain operating in more than 25 countries. The project developed a system for recognizing and tracking customer actions in stores using surveillance cameras which are used by analysts to optimize store operations. Our product was a mixture of deep convolutional networks for detection and classical algorithms for object tracking. The technology stack was based on Python and TensorFlow.