JobMatcherExerizon is redefining the traditional consulting model: we are not only a service provider but a partner who shares risk and success with clients. Why we are looking for you: We are building a data team from the ground up — you will co-decide the direction it takes. You will work on Data Lake and Data Lakehouse projects and data migrations supported by AI. AI helps us daily by automating repetitive, tedious tasks, leaving you with work that truly requires thinking and decision-making.
Exerizon is redefining the traditional consulting model: we are not only a service provider but a partner who shares risk and success with clients. Why we are looking for you: We are building a data team from the ground up — you will co-decide the direction it takes. You will work on Data Lake and Data Lakehouse projects and data migrations supported by AI. AI helps us daily by automating repetitive, tedious tasks, leaving you with work that truly requires thinking and decision-making.
2–4 years of experience (mid level) or 4+ years (senior); strong knowledge of the Databricks platform; solid SQL and Python skills; knowledge of at least one database engine: Oracle, PostgreSQL or MS SQL; solid understanding of data warehousing basics: loading processes and layered architecture; knowledge of data modeling techniques (Kimball, Data Vault); familiarity with data testing approaches and at least one tool such as dbt tests or Great Expectations; understanding of data lineage and data cataloging; practical knowledge of orchestration tools like Airflow, Dagster or dbt; basic knowledge of Azure or AWS in the context of data processing; ability to work with Git repositories; ability to document built solutions; independence in managing tasks, status, and priorities; curiosity in diagnosing data processing problems; openness to using AI tools such as Claude or GitHub Copilot; willingness to understand the business customers you deliver data to — not only the technology you work with. Nice to have: practical knowledge of dbt as a transformation tool (beyond tests) and Data
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2–4 years of experience (mid level) or 4+ years (senior); strong knowledge of the Databricks platform; solid SQL and Python skills; knowledge of at least one database engine: Oracle, PostgreSQL or MS SQL; solid understanding of data warehousing basics: loading processes and layered architecture; knowledge of data modeling techniques (Kimball, Data Vault); familiarity with data testing approaches and at least one tool such as dbt tests or Great Expectations; understanding of data lineage and data cataloging; practical knowledge of orchestration tools like Airflow, Dagster or dbt; basic knowledge of Azure or AWS in the context of data processing; ability to work with Git repositories; ability to document built solutions; independence in managing tasks, status, and priorities; curiosity in diagnosing data processing problems; openness to using AI tools such as Claude or GitHub Copilot; willingness to understand the business customers you deliver data to — not only the technology you work with. Nice to have: practical knowledge of dbt as a transformation tool (beyond tests) and Data
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Based on: Data Engineer
Насколько эта вакансия подходит вашему резюме
Войдите, чтобы увидеть AI match score и персональную ленту.
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