B2B

Python

remote

We are looking for a Senior Data Engineer to lead a team, improve data practices, and help drive new opportunities and growth.

Would you like to join an experienced team and work with a world-known Customer? We have a fantastic opportunity for an experienced and skilled Senior Data Engineer to participate in the development of a rapidly growing project.

Feel challenged? Then we would like to hear from you!

Customer

Our client is a leading global company with years of experience in innovative technologies. By bridging the real and digital worlds with top solutions, they enable the transformation of everyday life for billions of people. Their portfolio of products, services, and solutions is focused on clinical decision-making and treatment pathways.

Project

The project’s mission is to enable healthcare providers to increase their value by providing them with innovative technology and services in diagnostic and therapeutic imaging, laboratory diagnostics, molecular medicine, digital health, and enterprise services.

Requirements

Proven experience in data engineering and hands-on work with cloud computing services, specifically Microsoft Azure
Strong understanding of data analytics fundamentals, including dimensional modeling, ETL, reporting tools, data governance, data warehousing, and both structured and unstructured data
Proficiency in SQL and Python
Hands-on experience with Databricks and PySpark for data processing and modeling
Familiarity with big data platforms such as Snowflake, BigQuery, etc.; experience with Snowflake is a plus
Fluency in business English

Personal Profile

Excellent communication skills

Responsibilities

Work closely with the client (PO) and other Team Leads to clarify technical requirements and expectations
Provide support where Team Lead’s support is required
Implement architecture based on Azure cloud platforms (Data Factory, Databricks, and Event Hub)
Design, develop, optimize, and maintain squad-specific data architectures and pipelines that adhere to defined ETL and Data Lake principles
Discover, analyze, and organize disparate data sources and structure them into clean data models with clear, understandable schemas
Contribute to the evaluation of new tools for analytical data engineering or data science
Suggest and contribute to training and improvement plans related to analytical data engineering skills, standards, and processes
Participate in pre-sale activities and work with potential opportunities in the data direction