Technology Specialist Data Mapping

Heineken; Kraków; On-site; PLN 180,000 - 300,000; Full time

Job summary

HEINEKEN Kraków is seeking a data-focused engineer to join the Digital & Technology Team. You will design and implement data mapping, ML‑driven matching, and semantic models to improve data transformations across the platform.

Work with Databricks, PySpark, SQL and Python to build scalable pipelines, ensure data governance and collaborate with engineers to align with architecture standards.

The role emphasizes collaboration, documentation and delivering practical solutions for business needs.

Qualifications

• Hands-on experience delivering data transformations at scale using Databricks, PySpark, and SQL.
• Strong Python skills for data processing and automation.
• Experience applying ML methods to data transformation, matching, or semantic problems.
• Good understanding of data management concepts, data quality, semantics, modelling and data contracts.

Responsibilities

• Designing and implement automated data mapping solutions using metadata, semantics, and transformation logic.
• Translating source-to-target mappings into scalable, reusable data transformations.
• Contributing to the evolution of metadata‑driven mapping approaches, including lineage and semantic models.
• Supporting automation of mapping use cases such as standardisation, harmonisation, and matching.
• Designing and apply algorithmic and ML‑driven matching solutions to improve mapping automation.
• Implementing entity resolution techniques at scale using Python and PySpark.
• Developing and maintaining semantic models, including ontologies or knowledge‑graph–based structures.
• Assessing and refining matching approaches using quality metrics and practical performance considerations.
• Collaborating with data engineers to ensure mapping logic aligns with Databricks, Lakehouse, and Medallion principles.
• Applying knowledge of PySpark, SQL, Delta Lake, and Python to influence pipeline and transformation design.
• Ensuring mapping logic is scalable, transparent, and aligned with data governance standards.
• Contributing to shared CI/CD, testing, and deployment practices for data pipelines.
• Promoting reusable patterns, documentation standards, and best practices with the team.
• Working closely with data mapping specialists, analysts, and domain experts to deliver solutions.

Skills

Databricks

PySpark

SQL

Python

Machine Learning

Entity Resolution

Data Quality

Data Modelling

Data Governance

Knowledge Graphs

Graph Databases

Jira

Tools

Databricks

Job description

Digital & Technology Team (D&T)is an integral division ofHEINEKEN Global Shared Services Center. We are committed to making Heineken the most connected brewery. That includes digitalizing and integrating our processes, ensuring best-in-class technology, and embedding a data-driven culture. By joining us you will work in one of the most dynamic and innovative teams and have a direct impact on building the future of Heineken!

Would you like to meet the Team, see our office and much more? Visit our website:Heineken (heineken-dt.pl)

Your responsibilities would include:

Core Data Mapping & Automation

• designing and implement automated data mapping solutions using metadata, semantics, and transformation logic
• translatingbusiness definitions and source‑to‑target mappings into scalable, reusable data transformations
• contributingto the evolution of metadata‑driven mapping approaches, including lineage and semantic models
• supporting automation of mapping use cases such as standardisation, harmonisation, and matching.

Advanced Matching & ML‑Driven Capabilities

• designing and apply algorithmic and ML‑driven matching solutions to improve mapping automation
• implementing entity resolution techniques (e.g. similarity scoring, probabilistic matching) at scale using Python and PySpark
• developing and maintain semantic models, including ontologies or knowledge‑graph–based structures, to improve mapping quality and reusability
• assessing and refine matching approaches using quality metrics and practical performance considerations,

Alignment with Data Engineering & Platform Teams

• collaboratingwith data engineers to ensure mapping logic aligns with Databricks, Lakehouse, and Medallion architecture principles
• applying strong knowledge of PySpark, SQL, Delta Lake, and Python to influence pipeline and transformation design
• ensuring mapping logic is scalable, transparent, and aligned with data governance standards.

Engineering Practices & Collaboration

• contributingto shared CI/CD, testing, and deployment practices for data pipelines
• promoting reusable patterns, documentation standards, and technical best practices within the chapter
• working closely with data mapping specialists, analysts, and domain experts to deliver solutions that meet real business needs.

You are a good candidate if you have:

• hands‑on experience delivering data transformations at scale using Databricks, PySpark, and SQL
• solid Python skills for data processing and automation
• experience applying advanced analytical or machine‑learning methods to data transformation, matching, or semantic problems
• strong problem‑solving skills in designing algorithms for data quality, similarity, and entity alignment, rather than purely rule‑based transformations
• good understanding of data management concepts, including data quality, semantics, modelling, and data contracts
• experience working with metadata, lineage, and governance tooling
• familiarity with Azure‑based data platforms and enterprise data environments
• ability to collaborate effectively with platform and data engineers, focusing on data logic, algorithms, and analytical solutions rather than infrastructure or service ownership
• confidence explaining technical solutions to both technical and non‑technical stakeholders
• Python (incl. PySpark, data‑centric tooling)
• SQL (advanced), data transformations and modelling
• Data warehousing fundamentals and data governance
• Machine Learning techniques
• Jira
• Machine Learning techniques applied to data matching, classification, or similarity scoring
• Entity Resolution using probabilistic or ML‑based approaches
• Graph Databases and Knowledge Graph concepts.

Nice to have:

• semantic modelling, ontologies, or taxonomy‑based data modelling
• familiarity with ML libraries used in large‑scale data processing (e.g. Spark ML, custom Python models).

At HEINEKEN Kraków, we take integrity and ethical conduct seriously. If someone has concerns about a possible violation of legal regulations indicated in Polish Whistleblowing Act or our Code of Business Conduct, we encourage them tospeak up. Cases can be reported to global team or locally (in line with the local HGSS Whistleblowing procedure) by selecting proper option in this tool or by communicating it on hotline.