Lead Data Architect

Zartis is a global AI transformation and technology consulting partner where talented engineers and technologists work on cutting edge innovation.

We partner with ambitious organizations to design, build, and scale technology solutions that deliver real impact.

Our teams bring deep expertise in AI driven platforms, secure API architectures, and cloud native engineering.

You will work on meaningful projects that accelerate the adoption of advanced technologies, from strategy and discovery through to full product delivery, helping turn complex challenges into measurable outcomes.

With engineering hubs across EMEA and LATAM, and long term partnerships in financial services, healthcare and life sciences, and energy and climate, we offer opportunities to work on projects that truly matter.

Here, you will not just build technology, you will drive business impact and grow your career alongside industry leaders.

We are looking for a Lead Data Architect to work on a project in the media sector.

Our teammates are talented people that come from a variety of backgrounds.

We’re committed to building an inclusive culture based on trust and innovation.

You will be part of a distributed team developing new technologies to solve real business problems building a new canonical data platform.

The focus is on discovery and data modelling: understanding existing data, working with business and technical stakeholders, and defining a clear, consistent canonical model.

You will help map existing datasets and legacy structures into the target model, identify gaps and inconsistencies, document key decisions and work closely with engineers to turn the model into an actionable delivery plan.

Lead discovery and data modelling for a new canonical data platform.

Work with business users, domain experts, and engineering teams to understand data requirements, business concepts, and use cases.

Inventory and analyse existing datasets, feeds, schemas, and legacy data structures, particularly where documentation may be incomplete.

Define the grain, entities, relationships, semantics, and key measures of the canonical data model.

Establish clear and consistent definitions for key business entities and measures.

Map existing source data into the target canonical model, identifying gaps, inconsistencies, and data quality issues.

Document schemas, source-to-target mappings, lineage, data definitions, assumptions, and key architectural decisions.

Work closely with data engineering teams to turn the canonical model and mappings into implementable solutions.

Identify key risks, dependencies, and gaps within the data architecture and help define appropriate approaches to address them.

Help sequence and structure the work so engineering teams can execute in parallel once the model is sufficiently understood.

Act as the technical owner of the canonical data model throughout discovery and early delivery.

Strong experience in data architecture and data modelling, including canonical and dimensional modelling across complex business domains.

Proven ability to understand complex data landscapes and translate business concepts, source structures, and requirements into clear, coherent data models that engineering teams can build against.

Experience analysing and profiling data across multiple sources, including complex or poorly documented legacy environments, to establish structure, quality, relationships, meaning, and usage.

Strong understanding of data entities, relationships, grain, semantics, business measures, source-to-target mapping, and data lineage.

Strong SQL skills, with the ability to explore and profile source data as part of discovery and modelling activities.

Experience defining source-to-target mappings and identifying gaps, inconsistencies, and data quality issues across diverse data sources.

Experience translating business and technical stakeholder requirements into clear data definitions, models, mappings, and architectural decisions.

Strong stakeholder engagement skills, with the ability to move confidently between conversations with business users, domain experts, architects, and engineering teams.

Experience working closely with data engineering teams to ensure models and mappings can be translated into practical, implementable solutions.

Experience documenting complex data models, schemas, mappings, lineage, assumptions, and architectural decisions using appropriate data modelling and documentation tools.

Experience working with large-scale analytics or enterprise data platforms and an understanding of modern lakehouse architectures.

Exposure to cloud-based data ecosystems, ideally AWS and/or Snowflake; familiarity with AWS data services such as S3 or Glue would be advantageous.

Experience working with media, audience, ad-tech, or content analytics data would be advantageous.

Excellent communication and documentation skills, with the ability to bring structure and clarity to ambiguous and complex data problems.

Intellectual curiosity and a proactive approach to understanding how data is created, used, and interpreted across a business.

Experience with large-scale analytics platforms.

Experience with media, audience, or similarly complex behavioural data would be a plus.

Monthly payment as financial support for remote working.

We have established a career development program accessible for all employees with a 360º feedback that will help us to guide you in your career progression.

For Tech training at Zartis, you have time allocated during the week at your disposal.

You can request from a variety of options, such as online courses (from Pluralsight and Educative.io, for example), English classes, books, conferences, and events.

You can become a mentor in Zartis or you can receive mentorship, or both.

A platform that provides sessions with a range of specialists, including mental health professionals, nutritionists, physiotherapists, fitness coaches, and webinars with such professionals as well.

We organize tech events, webinars, parties, and activities to do online team-building games and contests.