Senior Data Engineer at iTeamly – Kraków | Jobyssey

About the Role

We are looking for an experienced Senior Data Engineer to take technical ownership of data solutions built around large-scale social and web data. The role combines hands-on engineering, data discovery, metric development, data quality, and rapid prototyping.

What we offer:

Fully remote work.
High level of technical ownership and autonomy.
Real influence on how new data sources, metrics, and technical approaches are developed.
Hands-on work with Python, SQL, dbt and Snowflake.
Opportunity to solve technically complex problems using large and diverse datasets.
Opportunity to combine engineering with experimentation, data discovery, and rapid prototyping.
Close collaboration with senior data leadership.
Opportunity to shape engineering and data quality practices within the workstream.
Access to modern AI-assisted development tools.
Multisport card.
Private healthcare.

Requirements:

5+ years of commercial experience in data engineering, analytics engineering, or another strongly technical data role.
Strong hands-on experience with Python and SQL.
Strong practical experience with dbt.
Experience working with Snowflake.
Experience building or developing reliable data pipelines and transformations.
Strong understanding of data quality and automated testing.
Ability to independently own complex technical problems from definition through implementation.
Experience translating business or product requirements into technical solutions.
High level of autonomy, technical judgement, and ownership.

Nice to have:

Experience working with social media, web, trend, or behavioural data.
Experience integrating external APIs and new data sources.
Experience with Airflow or other orchestration tools.
Experience with Data Vault modelling.
Experience with Tableau and/or Power BI.
Broader experience with data modelling and data warehouse concepts.
Experience designing analytical metrics and data products.
Experience building prototypes to validate new data approaches.
Experience introducing automated data quality checks and testing practices.
Experience assessing unfamiliar datasets for quality and business value.
Previous exposure to trend or signal analysis.
Practical experience with AI coding tools such as Copilot, Cursor, Codex, or Claude Code.

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