Technology Specialist Data Mapping

Heineken; Kraków; On-site; PLN 180,000 - 260,000; Full time

Benefits offered by this job

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Job summary

HEINEKEN Kraków, within the Digital & Technology Team, is seeking a Technology Specialist Data Mapping to drive automated data mapping capabilities. The role sits at the intersection of analytics engineering, data engineering, and data management, leveraging Databricks, PySpark, and SQL to deliver scalable solutions.

You will collaborate with data mapping specialists, data quality teams, and domain experts to build knowledge-graph–based structures and semantic models that elevate data governance

Qualifications

• Hands-on experience delivering data transformations at scale using Databricks, PySpark, and SQL.
• Experience applying advanced analytical or machine-learning methods to data transformation, matching, or semantic problems.
• Strong collaboration with data engineers and business analysts to deliver scalable data-mapping solutions.

Responsibilities

• Core Data Mapping & Automation: design and implement automated data mapping solutions.
• Advanced Matching & ML‑Driven Capabilities: build ML-driven matching and entity resolution at scale.
• Alignment with Data Engineering & Platform Teams: ensure mapping logic aligns with Databricks, Lakehouse, and Medallion architecture.
• Engineering Practices & Collaboration: promote CI/CD, testing, and documentation patterns across the team.

Skills

PySpark

Python

SQL

Databricks

Data mapping

Data governance

Knowledge graph

Tools

Databricks

Delta Lake

Azure

Jira

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 )

As a Technology Specialist Data Mapping(Data Analytics/Solutions Engineer), you will be driving the development of automated data mapping capabilities within the Data Mapping Chapter. Your role sits at the intersection of analytics engineering,data engineering, anddata management, enabling scalable, high‑quality data mapping artifacts. You will design, build, and operate backend and data solutions on Azure, working with modern platforms such asDatabricks,Azure DevOps, andUnity Catalog. You will collaborate closely with data mapping specialists, data quality specialists, data engineers, data business analysts, and domain experts to build resilient data mapping capabilities aligned with our enterprise data strategy. In addition, this role focuses on applyingadvanced analytical and machine‑learning techniquesto improve the automation and intelligence of data mapping processes, includingalgorithmic matching, entity resolution, semantic modelling, and knowledge graph‑based approaches.

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.

You are a perfect candidate if you also 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.