Data Engineer @ ITMAGINATION
(práce v Polsku)

Data Engineer @ ITMAGINATION

Polsko

• Strong SQL (performance, CTEs, window functions) and Python for data work.  
• Hands-on Snowflake (warehousing, performance, role/security basics) and dbt (models, tests, docs).  
• Proven experience delivering end-to-end pipelines (extract → transform → model → serve for analytics).  
• Ability to handle unstructured/inconsistent data and normalize across multiple source systems.  
• Experience collaborating with Product/Design/Engineering to define KPIs and metrics.  
• Comfortable with Git-based workflows (branching, PR reviews) and working in a distributed team.  
• Proactive, independent problem-solver; comfortable with ambiguity and iterative discovery.  
• English: B2/C1 (stakeholder communication).  

Nice-to-Have 

• Experience with Sigma (or strong BI fundamentals in Tableau/Power BI and eagerness to adopt Sigma).  
• Data quality frameworks (Great Expectations), data contracts, or semantic layers.  
• Orchestration tooling (Airflow/Prefect/dbt Cloud job orchestration).  
• Exposure to embedded analytics in product environments.  
• Familiarity with regex and text parsing techniques.  
• Cloud experience (AWS/GCP/Azure) around data services and IAM.  
• Basic understanding of ML/LLM-driven analytics workflows.  

ITMAGINATION, now part of a Virtusa Group helps its Clients by becoming a true extension of their software and data development capabilities. Through the readily set up, comprehensive, and self-governing teams, we let our Clients focus on their business while we make sure that their software products and data tools scale up accordingly and with outstanding quality. We are looking for team player to fill Data Engineer position in a dynamic international project.

,[​Design & build ELT pipelines to ingest and transform data into Snowflake using SQL/Python and dbt (modular, tested models).  , Model for analytics: create well-structured, analysis-ready datasets; define/maintain KPIs and semantic definitions.  , Harden data quality: implement tests, lineage, documentation, and CI checks in GitHub; monitor pipeline health.  , Solve messy data problems: normalize unstructured/inconsistent data (e.g., regex parsing, schema harmonization).  , Enable insights: partner with analysts and product teams to deliver metrics in Sigma; support dashboard consumers.  , Collaborate in Agile: work in epics/sprints; prioritize backlog; iterate with stakeholders; contribute to continuous improvement.  , Champion customer value: align transformations and metrics to real user expectations and product outcomes.  ] Requirements: Snowflake, SQL, Python, dbt, GitHub, Security, Git, BI, Tableau, Power BI, Airflow, Cloud, AWS, GCP, Azure, IAM Additionally: Udemy for business, Sport subscription, Private healthcare, International projects.

Kategorie

data

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