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Clariant

Data Engineer (m/f/d)

Clariant Łódź, Łódź Voivodeship, Poland · PLN 129K–PLN 215K/yr

Chemical Manufacturing · 10,001+ employees

Yesterday
data-engineer Mid (2-5 yrs) Full-time Poland
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About the role

Design and implement complex data pipelines to optimize data processing and storage across global engineering functions. Lead data modeling initiatives, drive system integration, and champion data governance to ensure high data quality and compliance.

What they look for

Data Engineering Data Pipelines Data Modeling System Integration Data Governance GenAI RAG Root Cause Analysis Process Optimization Data Quality Compliance Stakeholder Communication

Requirements

The role requires expertise in building data pipelines, developing sophisticated data models, and implementing AI integration patterns. Candidates should possess strong problem-solving skills and the ability to communicate technical insights to business stakeholders.

Full description

Job ID: 41832 | Location: Lodz, Poland | Expected Salary Range: zł129,000.00 - zł215,000.00 | Work Model: Hybrid

We are looking for a Data Engineer to design, implement, and optimize data pipelines that power efficient data processing, storage, and analysis across our global organization. You will be embedded in our Engineering & Process Technology function, working at the intersection of data, business operations, and chemical industry processes. You will play a key role in shaping our data landscape by driving governance initiatives, integrating systems, and advancing data capabilities through cutting-edge technologies. If you are passionate about translating complex data challenges into real business value, we would love to hear from you.

What will you be doing?

  • Data Pipeline Development – Design and implement complex data pipelines that meet evolving business requirements across engineering and process functions.
  • Process Optimization – Develop and execute optimization strategies for existing data processes to improve efficiency and performance in an industrial context.
  • Data Modeling – Lead the development of sophisticated data models and schemas across various business and operational domains.
  • System Integration – Drive end-to-end data integration solutions across multiple systems and platforms.
  • Data Governance – Initiate and champion data governance initiatives to ensure data quality and compliance.
  • AI Integration – Implement GenAI integration patterns and RAG pipelines to enhance data capabilities.
  • Problem Resolution – Resolve complex data-related issues through systematic root cause analysis.
  • Stakeholder Communication – Act as a bridge between technical teams and business stakeholders, translating data insights into actionable outcomes for non-technical audiences.

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