IT Data Architect

• Define and evolve conceptual, logical, and physical data models across core business domains.
• Establish canonical and semantic models, shared dimensions, and reference data to enable consistent reporting and cross-functional integration.
• Design reusable business entities, metrics, and data structures that support enterprise-wide consistency and scalability.
• Partner with business stakeholders to translate processes, KPIs, and analytical needs into effective data models and semantic layers.
• Recommend fit-for-purpose modeling approaches based on business and technical requirements.

• Define standards for data products, including structure, naming, documentation, versioning, quality expectations, and lifecycle management.
• Develop reusable patterns for data ingestion, transformation, and consumption that can be scaled across teams and use cases.
• Partner with data engineering teams to create reference architectures, templates, and reusable implementation assets.
• Establish schema design and evolution practices that support ongoing change while minimizing downstream impact.
• Contribute to iterative solution delivery through Agile and DevOps practices.

• Design end-to-end data pipeline architectures for batch, near real time, and event-driven use cases.
• Define logical and physical architecture patterns using cloud-native platforms and modern data technologies.
• Guide the development of scalable, secure, reliable, and cost-effective data solutions using tools such as Databricks, dbt, SQL, Python or Scala, and orchestration frameworks.
• Ensure solutions are built with strong observability, testing, performance, and operational reliability.
• Collaborate with engineering teams to support production-ready implementation and continuous improvement.

• Embed data governance, security, compliance, lineage, metadata, and retention requirements into architecture and design decisions.
• Define and promote standards for data quality, validation, cataloging, access controls, and engineering best practices.
• Provide technical leadership and mentoring to data engineers, analysts, and architects to drive consistent adoption of standards and patterns.
• Evaluate emerging technologies and architectural approaches and make recommendations aligned to business priorities.

• 7–10 years of relevant IT experience, with at least 4+ years in a Data Architect, Senior Data Engineer, or similar role focused on data design and modeling.
• 5+ years of experience designing and implementing data models for analytics and operational reporting (dimensional, normalized, and/or domain-driven designs).
• 5+ years of experience building and/or guiding the development of ETL/ELT pipelines in cloud or modern data platforms.
• 4+ years of hands-on experience with Databricks or similar cloud-based lakehouse platforms supporting enterprise data lakes, data warehouses, and business intelligence
• 2+ years of experience using dbt (or similar transformation frameworks) including model structuring, tests, and documentation.
• Strong proficiency in SQL and experience with at least one programming language used in data engineering (Python or Scala preferred).
• Experience using Git and related DevOps tooling (GitHub/GitLab/Bitbucket, CI/CD pipelines such as GitHub Actions, Azure DevOps, Jenkins, etc.).
• Experience with data catalog/lineage/metadata tools is a plus (e.g., Collibra, Alation, Unity Catalog, Purview).
• Familiarity with modern data architecture paradigms such as data mesh and domain-oriented architecture

The successful candidate for this position will have the following general experience and expertise:

• Ability to navigate ambiguity, make decisions with incomplete information, and drive consensus.
• Strong problem-solving skills with a structured, analytical approach.
• Highly self-motivated, organized, and able to manage multiple priorities.
• Experience working with distributed and/or international teams is a plus.
• Strong communication and presentation skills, with the ability to engage technical and non-technical audiences effectively

Travel : Limited work travel