Senior Data Engineer
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered the largest Data Science Community in Eastern Europe, boasting a network of over 30,000 AI top engineers.
About the client:
We are working with a new generation of data service provider, specializing in data consulting and data-driven digital marketing, dedicated to transforming data into business impact across the entire value chain of organizations. The company’s data-driven services are built upon the deep AI expertise the company’s acquired with a 1000+ client base around the globe. The company has 1000 employees across 20 offices who are focused on accelerating digital transformation.
About the role:
We are seeking a Senior Data Engineer (Azure) to design and maintain data pipelines and systems for analytics and AI-driven applications. You will work on building reliable ETL/ELT workflows and ensuring data integrity across the organization.
Required skills:
– 6+ years of experience as a Data Engineer, preferably in Azure environments.
– Proficiency in Python, SQL, NoSQL, and Cypher for data manipulation and querying.
– Hands-on experience with Airflow and Azure Data Services for pipeline orchestration.
– Strong understanding of data modeling, ETL/ELT workflows, and data warehousing concepts.
– Experience in implementing DataOps practices for pipeline automation and monitoring.
– Knowledge of data governance, data security, and metadata management principles.
– Ability to work collaboratively with data science and analytics teams.
– Excellent problem-solving and communication skills.
Responsibilities:
– Transform data into formats suitable for analysis by developing and maintaining processes for data transformation;
– Structuring, metadata management, and workload management.
– Design, implement, and maintain scalable data pipelines on Azure.
– Develop and optimize ETL/ELT processes for various data sources.
– Collaborate with data scientists and analysts to ensure data readiness.
– Monitor and improve data quality, performance, and governance.
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