Position: Data Engineer
Company: NCube
Experience required: 3y
English level: upper

Our client has been recognized as a great workplace by Glassdoor and Great Place to Work and was recently named to the Forbes Fintech 50 and the Red Herring 100 lists of most innovative companies.

Product — a platform that lets everyone from first-time investors to global asset managers evaluate, purchase, and own residential investment properties with confidence from anywhere in the world.

If you thrive in a team environment, are willing to pitch in wherever needed to help the team succeed, are passionate about data, and excited about empowering users with data to drive decision making, this is the place for you.

Experience / Skills:

Must have:

3+ years of professional experience in writing production Python code, shell scripts, and complex SQL

3+ years of professional experience building robust data pipelines beyond simple API pulls

1+ year building and deploying data-related infrastructure (messaging, storage, compute, transform, execution via docker, and/or CI/CD pipelines across dev/stage/prod

Experience in data warehousing and dimensional data modeling

Experience with Airflow (or similar tools), AWS, Azure, and DBT is a plus

BS or MS in a technical field: computer science, engineering or similar

Good English

Strong communication and interpersonal skills

We offer: 

Excellent career opportunities

Competitive remuneration based on qualification and contribution

Flexible working schedule, vacations, paid sick leaves

Good working environment

Great team spirit

Perfect office location in the very center of Kyiv

Responsibilities: 

Design, implement and deploy, scalable, fault-tolerant pipelines that ingest, and refine large diverse (structured, semi-structured and unstructured datasets) into simplified, accessible data models in production

Built departmental data-marts for supporting analytics across the company

Collaborate with cross-functional teams to understand data flows and processes to enable the design and creation of the best possible solutions

Provide quality data solutions in a timely manner and be responsible for data governance and integrity while meeting objectives and maintaining SLAs

Build tools and fundamental data sets that encourage self-service

Improve and maintain the data infrastructure