Data / Analytics Engineer (Microsoft Fabric, Power BI)

150 - 200 PLN/ godz./ godz.B2BB2B (netto)

MidZdalnie·WarszawaInne·B2B

#472871·Dodano 2 dni temu·5

Źródło: justjoin.it

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Tech Stack / Keywords

DataMicrosoft FabricPower BIEnglishSQL Python

Podsumowanie oferty

The project focuses on measuring adoption and value delivered by AI initiatives. You will build data ingestion and transformation pipelines in Microsoft Fabric, develop a semantic model and Power BI reporting, and help design conversational data access using LLMs. You will also translate business questions into measurable KPIs and validate data-driven answers.

Wymagania

Practical experience with Microsoft Fabric components (Lakehouse/Warehouse, pipelines, Dataflows Gen2, notebooks).
Advanced Power BI skills, including data modeling, DAX, and building semantic models.
Proficient SQL skills and experience with Python or PySpark for data processing.
Experience integrating data from multiple sources (APIs, files, databases, SaaS).
Experience or demonstrable interest in GenAI solutions over data: text-to-SQL/DAX, Fabric data agents or Copilot, and familiarity with prompt engineering and assessing LLM response quality.
Ability to communicate effectively with business stakeholders and independently clarify ambiguous requirements.
Business-oriented thinking with a focus on KPIs and measurable value delivered.
Proactive and autonomous working style; fluent English for collaboration with a US-based client.
Positive, personable attitude; a sense of humour is welcome.

Obowiązki

Design and implement data flows in Microsoft Fabric, including ingestion, transformation, and data modeling across multiple sources.
Develop and maintain the semantic model and initial Power BI reports as the first project phase.
Collaborate with business stakeholders to define KPIs for AI adoption and benefits, and translate those KPIs into measurable data metrics.
Prototype and implement conversational data access using technologies such as Fabric data agents, Copilot in Power BI, or custom solutions leveraging LLMs.
Establish validation and quality assurance practices for conversational answers: create test question sets, verify correctness, and define clear boundaries for appropriate queries.
Integrate data from diverse sources (APIs, files, databases, SaaS) while maintaining data integrity and lineage.
Produce clear documentation and communicate technical decisions and limitations to business stakeholders in English.

Benefity

Outstanding integration trips to various places in Europe for all employees.
Professional growth through exposure to modern data engineering patterns, Power BI semantic modeling, and LLM-driven solutions.

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