Data Engineer (#1232)

BI & Machine Learning Department in Remote (Ukraine)

Location

Remote (Ukraine) Your expertise Strong analytical and problem-solving skills with the ability to work with complex datasets and identify practical solutions Strong SQL skills and experience working with SQL-based databases data warehouses or data lakehouse technologies Practical experience designing and building ETL / ELT pipelines from scratch Proficiency in Python for data processing automation and building data engineering solutions Good understanding of data modeling concepts and experience designing data structures for analytics Experience with dbt or similar data transformation tools Experience with cloud platforms preferably AWS Ability to work independently as well as collaborate effectively with other engineers BI specialists and business stakeholders Intermediate level of English Will definitely be a plus Hands-on experience with Trino Experience with Apache Iceberg or other data lakehouse technologies Experience with dbt Core Experience with Apache Airflow or other workflow orchestration tools Experience with DataHub or similar data catalog metadata lineage or semantic layer solutions Experience with AWS data services such as S3 Glue Athena or similar Understanding of data warehouse and data lake architecture Experience with CI / CD and version control practices for data engineering projects General understanding of BI platforms such as Tableau Power BI or similar tools What’s in it for you? Remote work format with opportunities for business trips Competitive base salary + performance-based bonus Dynamic and ambitious environment where you can directly influence growth and be rewarded for success Developed corporate culture no micromanagement culture based on principles of truth trust and transparency “You build it you own it” mentality in most contexts Support of personal and professional development + support of experienced colleagues + in-house events and trainings + regular knowledge sharing in teams + English classes and speaking clubs Life-balance support + truly flexible schedule no time-tracking at all + 25 working days of vacation + 5 days of paid sick leave per month (if necessary) without providing a medical certificate + generous maternity / paternity leave program Professionally strong environment friendly and open atmosphere ability to influence the product development and recognition for it You will be involved into Design and build scalable data pipelines and ETL / ELT processes Develop and maintain data models using dbt Core Build and orchestrate data pipelines using Apache Airflow Work with Trino and Apache Iceberg to build and query data lakehouse solutions Use AWS services to develop and operate cloud-based data solutions Develop Python-based data processing and automation components Build and maintain data catalogs metadata and data lineage using DataHub or similar solutions Collaborate with BI specialists and other technical teams to understand requirements and deliver data solutions Contribute to the architecture and development of a new data platform from the ground up Monitor troubleshoot and continuously improve data pipelines and data quality About the company and project Namecheap was founded in 2000 on the idea that all people deserve value-priced domains delivered through stellar service. Today Namecheap is a leading ICANN-accredited domain name registrar and web hosting company with over 17 million domains under management. Our culture is built on the values that we live every day the way we work the way we collaborate with our global network of colleagues and the way we relentlessly innovate solutions that meet the emerging needs of our customers. We are a Business Intelligence team solving business challenges through innovative technology solutions. We have extensive experience building BI ETL data warehouse and ML solutions. We are currently working on a new data platform and are looking for a Data Engineer to join the team and take part in building the solution from scratch. You will work on designing and developing data pipelines data models and analytics infrastructure using modern cloud and data engineering technologies.