Senior Data Engineer Contractor

Join a diverse team of approx. 2000 professionals across four continents, driving innovation and growth within intive’s technology hubs. Work alongside industry experts trusted by leading brands like Audi, BMW, Deichmann, Meta, NewsCorp, Tandem, Paramount, Vorwerk, and Warner Bros. Discovery to create pioneering, sustainable digital experiences.

At intive, agile thinking and deep industry expertise come together across Automotive & Mobility, Commerce, Financial Services, Healthcare & Life Sciences, and Technology, Media & Communication. Be part of a team that’s shaping the future of digital innovation.

Scope of Services:

• Build and manage robust data pipelines, storage solutions, and data refresh processes across Microsoft Fabric and AWS to support analytics and reporting.
• Develop dbt models across source, staging, intermediate, and mart layers, incorporating reusable business logic, facts, dimensions, testing, freshness checks, documentation, lineage, and exposures.
• Create reliable and high-performing datasets and semantic models for Power BI and self-service analytics.
• Develop and maintain data modelling standards covering Kimball dimensional modelling, consistent business keys, conformed dimensions, and clearly defined entities.
• Maintain data quality through automated validation, monitoring, alerting, and root-cause analysis.
• Keep comprehensive documentation covering data pipelines, datasets, definitions, assumptions, lineage, and runbooks.
• Support secure and governed access to data while applying engineering best practices such as version control, code reviews, environment separation, and controlled releases.

Services Experience:

• Service Experience as a Data Engineer or Analytics Engineer delivering reliable pipelines and curated datasets for BI.
• Strong and practical service experience with dbt (model layering, tests, sources, freshness, docs, lineage, exposures, and safe model change practices), SQL, Kimball dimensional modelling skills, AWS data services and cloud data engineering patterns, Microsoft Fabric (Lakehouse, Warehouse).
• Service Experience with data quality, monitoring, operational support, and troubleshooting.
• Good communication with technical and non-technical stakeholders.