Data Engineer
Role details
Job location
Tech stack
Job description
We are looking for a Data Engineer with 2-3 years of experience in managing, processing, and analyzing large data volumes to enhance the quality, availability, and usability of information across the organization.
The role will provide support to data and reporting projects, as well as to initiatives that incorporate advanced analytics or AI components. This position requires strong technical expertise, analytical thinking, and a collaborative mindset to work effectively with business and technology teams.
What You Will Do
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Design, maintain, and optimize databases and data ingestion/transformation pipelines.
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Ensure the quality, integrity, and consistency of data collected from multiple sources.
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Develop ETL/ELT processes and automate information flows.
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Collaborate with business and technology teams to facilitate data use in reports, dashboards, and analytical systems.
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Contribute to the definition of efficient data models and structures for high-volume environments.
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Support the integration of analytical or AI tools on top of existing data infrastructures., This is an exciting opportunity to join a dynamic and technology-driven company, where you will contribute to building robust data architectures and enabling data-driven decision-making. You will work on impactful projects, collaborate with cross-functional teams, and gain hands-on experience in an environment that combines analytics, innovation, and advanced technology
Requirements
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2-3 years of experience in Big Data, Data Engineering, or Database Administration.
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Strong proficiency in SQL and database engines (MySQL, ClickHouse, Elasticsearch, etc.).
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Experience working with distributed or large-scale storage environments (e.g., MinIO, S3).
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Proficiency in Python or Shell scripting for automation.
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Solid understanding of ETL processes, data transformation, and cleansing.
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Basic knowledge of data security and governance principles.
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Fluent in English (written and spoken).
Valuable Experience
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Experience with dashboarding or data visualization tools (Grafana, Metabase, Power BI, etc.).
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Familiarity with NLP or semantic analytics to support AI-related projects.
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Experience with cloud infrastructure or containerization tools (AWS, Docker, Kubernetes).