Lithuania
Data Analytics Engineering
Looking to build industry-relevant skills and launch your career in AI-native Data & Analytics? Apply for this program and shape your professional path with EPAM experts.
Price
Free
Program start
October
Learning type
Facilitated
Language
English
Duration
29 weeks
Format
Online
Level
Fundamentals
Details
If you are interested in creating data products and exploring the power of data to turn raw information into valuable insights for business growth, this expert-led program is for you.
Join us to build or expand your skills through immersive, hands-on learning in three core areas, while exploring how AI is transforming modern workflows:
- Data Integration. Master the art of developing and managing diverse data transformations and migrations.
- Data Visualization. Discover how to transform raw data into meaningful insights and present them through clear, engaging dashboards for better decision-making.
- Data Quality. Learn to ensure data is trustworthy, complete and accurate by testing the work of other data engineers, performing validations and re-creating logic to confirm it functions as expected.
After successfully completing all program stages, you will gain market-oriented skills that you may further apply at EPAM or elsewhere in tech.
Data Analytics Engineering
Data Analytics Engineering is about turning raw data into valuable business insights. It includes data collecting, storing, processing, validation and visualization.
What do we offer?
- Industry‑focused learning with EPAM, a leader in AI transformation engineering and consulting
- Free access to materials and resources designed and regularly refreshed by practitioners
- Intensive, hands-on training through numerous practical assignments
- Regular guidance and actionable feedback from professionals
- Modern educational ecosystem supported by built‑in AI tools
- Expansion of your core expertise alongside emerging technologies such as AI and Cloud
- Opportunity to join the EPAM team after successfully completing all stages
Training process
The program is designed to guide you through two engaging stages:
Stage 1: Fundamentals (3 months with ~15-20 hours per week)
At this stage, you'll build a strong foundation in Data Analytics Engineering. Here's what to expect:
- Weekly learning. Explore self-study materials, then practice through tasks and tests. Each assignment will come with a one-week deadline, helping you make steady progress.
- Guidance from mentors. Submit your practical assignments for feedback and approval from experts on a weekly basis.
- Q&A sessions. Join weekly evening meetings to discuss your questions and gain professional insights. Recordings will be available.
- Skill assessments. Participate in 2 short individual assessments, including theoretical reviews and live coding exercises.
Perform well to pass the technical interview and advance to the next level.
Stage 2: Specialization at EPAM Lab (4 months with ~20-30 hours per week)
This stage is all about taking your skills to the advanced level with a more intensive approach:
- Daily learning. Master in-depth materials and complete new practical assignments with further review every two days.
- Ongoing support. Attend daily group Q&A sessions with peers and mentors for real-time insights.
- Skill assessments. Participate in short individual assessments after each block, including theoretical reviews and live coding exercises.
Upon successful completion of both fundamentals and specialization stages, we will consider you for open entry-level positions based on your demonstrated skills and available opportunities at EPAM.
What is required for training:
General requirements:
- Citizens of Lithuania and permanent residents who are eligible to work in this country
- Second-to-last or final year university students and recent graduates
- Individuals aged 18 years and older
Skills requirements:
- English level from B2 (Upper-Intermediate) and higher
- Basic knowledge of Relational Database Management System (DBMS) theory
- Understanding of Structured Query Language (SQL)
- Familiarity with Python basics
Nice to have:
- Fluency in Lithuanian of C1 (Advanced) level and higher
- Degree from a technical university or other educational institution with a technical specialization
- Experience in banking and technical spheres
How to get started?
- Register on this page. Once you have filled in all the required fields and attached your CV, you will find a confirmation with more details in your mailbox and the notifications tab.
- Take the English test available in your account's "My assessments" section. You should complete it within 3 days of registration and achieve a B2-level result or higher to continue.
- Take the entry technical test available in your account's "My assessments" section. You should take it within 5 days of registration and reach a minimum score of 60% to continue (materials for preparation).
- Have a conversation with our AI assistant available in your account's "My assessments" section. This step will be available to shortlisted candidates after the review process, with 5 days to complete it once it is unlocked.
- Start studying a recommended self-paced course. You will get the link in the registration confirmation email and can explore the modules to prepare for the final assessment and upcoming program.
- Pass an online final technical assessment on September 24. Closer to this date, we will share additional details if you are selected to participate in this step.
- Receive an invitation to the program. We will share final enrollment results and further instructions for your learning journey within 1-2 weeks after the technical assessment.
Please check your inbox regularly to ensure you get all important notifications.
What will you learn?
Our program will cover both fundamental and advanced skills in Data Analytics Engineering:
Stage 1: Fundamentals (3 months with ~15-20 hours/week)
Stage 2: Specialization (4 months with ~20-30 hours/week)
Important notice
- Registration, assessment and program start dates are subject to change. Check for updates on this page and via email.
- We have designed this course for IT beginners seeking to master skills in Data & Analytics. If you have some production experience, feel free to explore our career website for job opportunities.
- The program has limited seats, which makes enrollment competitive. Completing all tests does not automatically guarantee an invitation to the interview and final assessment. We will prioritize applicants with the highest scores who meet all requirements.
- We strive to maintain a fair registration and testing process for everyone. If we detect any cheating, we will have to reject your application.
- If you are interested in applying while enrolled in another EPAM Campus program, please discuss it with your training coordinator first.
Useful links
Our graduates about us
“If you're open to reinventing your professional path, the journey leads to rewarding places you had.”
I spent years analyzing linguistic data and teaching others how to do it. Then, I walked into the Data Analytics Engineering program and realized what I needed was to "get my hands dirty" in the engine room.
SQL and Python felt impossibly foreign at first, but now they are the foundation I use daily. I wasn't just learning to code, I was learning to think in code.
Was starting the program worth it? Absolutely. Yes, there were challenges – new concepts to grasp, confidence to build. But if you're open to reinventing your professional path, the journey leads to rewarding places you hadn't imagined.
Volha Harytskaya
Data Quality Engineer
“I came for the knowledge and stayed for the community.”
I hadn’t heard about EPAM before, but the mentors were true ambassadors – they completely filled that gap for me. At first, I wasn’t focused on switching careers, more interested in staying updated with new technologies, but as I learned more about EPAM projects, world’s top clients and global team of experts, I was hooked.
I struggled a bit with balancing the Lab and my previous assignments, but thanks to the strong community of students and mentors who became like real buddies, I made it to the final stages and accepted the offer.
Vaidotas Sruogis
Data Quality Engineer
“Everyone was talking about AI, but I became curious about the data behind it.”
Before EPAM Campus, I already had a development background, but it wasn't related to Data. At first, studying felt quite easy. Then, step by step, it became more challenging – and much more interesting. My favorite part was building a data project from scratch and presenting a working solution. That was my "Yes, I can actually do this" moment. Campus taught me more than technical skills. Supportive mentors, practical advice, presentations, and technical interviews helped me understand how real projects work. I learned how to learn fast, approach new tasks thoughtfully, and see complex problems as interesting challenges rather than something intimidating. The program led to my first role at EPAM and gave me confidence that I can solve difficult problems.
Kateryna Zhabotynska
Junior Software Engineer
“I came for knowledge to "get my foot in the door of data," but gained confidence to "kick it open"”
I was trying to get my foot in the door of Data when I saw a post on LinkedIn. Enrolling felt like opening Pandora's box. I was impressed by the company’s global expertise and the quality of the program, offering well-rounded knowledge and practical tasks. Along with the knowledge, I gained confidence in myself – it didn’t feel like I just "got my foot in the door" – I kicked it open.
For future students, I recommend starting early by exploring EPAM self-study resources, preparing to commit time, especially in the later stages, and actively participating in Q&A sessions and discussions with mentors and peers.
Andrius Rusinas
Data Quality Engineer
“ Choosing EPAM Campus felt natural – the program is well-structured, practical and industry-aligned.”
My path to data started during my 2nd year at university. Out of all the subjects I had taken, "Databases" was the only one that truly captured my interest. I wanted to deepen my knowledge beyond academic theory, so I started exploring different learning opportunities. That's how I found the Data Analytics Engineering program at EPAM Campus. Choosing EPAM Campus felt natural – the program is well‑structured, practical, and aligned with real industry needs. The application process was quite competitive, but after the interview I was fortunate to be selected as a trainee, and that moment became the start of a new chapter. The learning process wasn't always easy – there were moments when the workload felt intense, and balancing Campus studies with university required strong organization. But the support from mentors, the structured curriculum, and the feeling of constant progress helped me overcome every challenge.
Anna Levchenko
Junior Software Engineer
“I was genuinely blown away by the quality of the learning materials and the practical tasks”
After my informatics studies at university, while I knew I wanted to work with data, I still felt like I didn't have a good picture of what day-to-day work would look like, what skills would be needed, and, above all, I did not feel very confident in myself. Which is why EPAM Campus, which I learned about almost by accident, felt like the perfect opportunity for me – and it basically ended up "answering" all the questions I had prior to joining the program. I was genuinely blown away by the quality of the learning materials and the practical tasks, all of which really helped build my understanding of data practices. Overall, I felt like there was a clear goal of fostering a culture of growth and learning – the mentors were really nice and understanding, which is why even the "examinations" didn't feel too scary.
Mantas Maciukas
Junior Data Quality Engineer