Technology Specialist Data Mapping

Digital & Technology Team (D&T) is an integral division of HEINEKEN 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!

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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, and data 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 as Databricks, Azure DevOps, and Unity 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 applying advanced analytical and machine‑learning techniques to improve the automation and intelligence of data mapping processes, including algorithmic 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
  • translating business definitions and 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.

• 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

  • collaborating with 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

  • contributing to 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:

• strong experience in data engineering, analytics engineering, or data platform work

• 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

• excellent written and verbal English.

Tech Stack:

• Python (incl. PySpark, data‑centric tooling)

• SQL (advanced), data transformations and modelling

• Databricks & Delta Lake (Lakehouse)

• Data warehousing fundamentals and data governance

• Metadata‑driven architectures (lineage, semantics, documentation)

• Azure data platforms (ADLS, ADF)

• Azure DevOps (Repos, pull requests, pipeline usage)

• Machine Learning techniques

• Jira

Strong plus:

• 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).

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