Data Engineer

The Data Engineer builds and helps govern the common data layer that divisions across HOYA share, together with the reusable data engineering assets they rely on to deliver trusted data for analytics, AI and operational use. The role does not operate division data solutions in production. Instead, it develops reference pipelines, ingestion frameworks, medallion Lakehouse patterns, data-product templates and engineering standards that division IT teams adapt to their own sources and use cases and joins divisions hands-on in selected proofs of concept if needed. Working on the Microsoft Azure stack, the Data Engineer proves each pattern across the full data lifecycle, from source assessment, ingestion and transformation through modelling, quality, metadata, lineage, serving and observability. The role partners with source-system owners, architects, platform teams, and division teams, to turn fragmented approaches into consistent, reusable enterprise patterns.

Job Responsibilities

• Work with analytics, AI, data governance and division teams to identify common data engineering needs and shape the outcomes into reusable designs, data contracts and standards.
• Design and build reusable ingestion frameworks for batch, near-real-time, streaming etc. which divisions can configure for their own enterprise and approved sources.
• Develop, test and maintain modular, metadata-driven and parameterized ETL/ELT pipeline templates using Microsoft Fabric, Azure Data Factory and Azure Data Lake Storage, in line with approved architecture patterns.
• Define and demonstrate the medallion Lakehouse architecture across bronze, silver and gold layers, with reference dimensional, normalized and domain-oriented models and curated data-product templates for analytics, AI and operational consumption.
• Build reusable data-quality components for reconciliation, validation, anomaly detection and issue management, with clear ownership, thresholds and service expectations defined in the accompanying standards.
• Define how metadata, classification, lineage, cataloguing, retention and access control are embedded using Microsoft Purview, and build this into the reference patterns.
• Demonstrate DataOps standards, including Git, automated testing, CI/CD, infrastructure as code, environment promotion, release controls and rollback.
• Build reusable observability patterns for pipeline health, data freshness, volume and schema changes, failures, performance and cost, and guide divisions on incident troubleshooting and root-cause analysis.
• Define optimization guidance for storage, compute, partitioning, file formats, workload design and orchestration, so that division solutions stay performant, reliable and cost efficient.
• Embed data protection into the patterns through managed identities, encryption, secrets management, network controls, least privilege, masking and approved data-sharing designs.
• Document the patterns, templates and standards thoroughly, including data models, interfaces, operating procedures and adoption guidance for division teams.
• Support HOYA's data-catalogue, master-data and data-product initiatives, and help division teams adopt and extend the group assets.

Qualifications

• Bachelor's degree in computer science, information systems, data engineering, software engineering or a related discipline.
• At least 8 years of hands-on experience designing, building and operating modern enterprise data solutions, including production workloads on Azure.
• Professional proficiency in English and the ability to communicate effectively with technical teams, data owners, stewards and business stakeholders.
• Ability to work effectively in a cross-functional, multicultural and geographically distributed enterprise environment.
• Microsoft certifications such as Fabric Data Engineer Associate are preferred.

Experience

• Extensive hands-on expertise with Microsoft Fabric and Azure data services, including Azure Data Factory and Azure Data Lake Storage.
• Strong SQL, Python and PySpark skills, with extensive experience in ETL/ELT, orchestration, data transformation and performance tuning.
• Experience engineering structured, semi-structured, streaming and API-sourced data using formats and interfaces such as Delta, Parquet, JSON, CSV, REST APIs and event streams.
• Strong data-modelling capabilities across medallion Lakehouse, dimensional, normalized and curated data-product patterns.
• Hands-on experience implementing data quality, metadata, lineage, security, observability and production monitoring.
• Knowledge of Microsoft Purview, identity and access management, encryption, privacy and enterprise data-governance practices.
• Experience with Git, automated testing, CI/CD, infrastructure as code and controlled deployment across development, test and production environments.
• Experience with Event Hubs, Kafka or comparable streaming services to support streaming analytics or real time AI use cases.
• Experience integrating data from enterprise platforms such as SAP, Salesforce, Dynamics 365 and ServiceNow is preferred.
• Experience supporting analytics and AI workloads in a large global enterprise is preferred.