Digital & Technology Team (D&T) is an integral division of HEINEKEN Global Shared Services Center. The team is committed to making Heineken the most connected brewery by digitalizing and integrating processes, ensuring best-in-class technology, and embedding a data-driven culture.
As a Technology Specialist Data Mapping (Data Analytics/Solutions Engineer), you will drive the development of automated data mapping capabilities within the Data Mapping Chapter. You will design, build, and operate backend and data solutions on Azure, working with platforms such as Databricks, Azure DevOps, and Unity Catalog.
You will collaborate with data mapping specialists, data quality specialists, data engineers, data business analysts, and domain experts to build resilient data mapping capabilities aligned with the enterprise data strategy. The role also applies advanced analytical and machine-learning techniques to improve automation and intelligence in data mapping processes, including algorithmic matching, entity resolution, semantic modelling, and knowledge graph-based approaches.
Responsibilities
- Design and implement automated data mapping solutions using metadata, semantics, and transformation logic.
- Translate business definitions and source-to-target mappings into scalable, reusable data transformations.
- Contribute to metadata-driven mapping approaches, including lineage and semantic models.
- Support automation use cases such as standardisation, harmonisation, and matching.
- Design and apply algorithmic and machine-learning-driven matching solutions.
- Implement entity resolution techniques, including similarity scoring and probabilistic matching, using Python and PySpark.
- Develop and maintain semantic models, including ontologies and knowledge-graph-based structures.
- Assess and refine matching approaches using quality metrics and performance considerations.
- Collaborate with data engineers to align mapping logic with Databricks, Lakehouse, and Medallion architecture principles.
- Apply PySpark, SQL, Delta Lake, and Python to pipeline and transformation design.
- Contribute to CI/CD, testing, and deployment practices for data pipelines.
- Promote reusable patterns, documentation standards, and technical best practices.
- Work with analysts and domain experts to deliver solutions addressing business needs.
Requirements
- Strong experience in data engineering, analytics engineering, or data platform work.
- Hands-on experience delivering data transformations at scale using Databricks, PySpark, and SQL.
- Strong 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 data quality, similarity, and entity alignment algorithms.
- Good understanding of data quality, semantics, modelling, data management, and data contracts.
- Experience with metadata, lineage, and governance tooling.
- Familiarity with Azure-based data platforms and enterprise data environments.
- Ability to collaborate with platform and data engineers.
- Excellent written and verbal English.
Tech stack
Python, PySpark, SQL, Databricks, Delta Lake, Azure data platforms including ADLS and ADF, Azure DevOps, Jira, data warehousing, data governance, metadata-driven architectures, and machine-learning techniques.
Additional advantages
- Machine learning applied to data matching, classification, or similarity scoring.
- Entity resolution using probabilistic or machine-learning-based approaches.
- Graph databases and knowledge graph concepts.
- Semantic modelling, ontologies, or taxonomy-based data modelling.
- ML libraries for large-scale data processing, such as Spark ML or custom Python models.
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