About the role

Snowflake Data Architect

Job Summary

The Data Architect to lead an enterprise-scale cloud migration and establish an Enterprise Data Model (EDM) from first principles, including guardrails, standards, definitions, and naming conventions. The role will model and deliver the first few domains, starting with Underwriting (UW) and then progressing to Claims, while enabling a first use case to replicate existing reports with reconciliation, quality checks, and governed data product delivery. The target platform is Azure + Databricks, with governance using Unity Catalog and Microsoft Purview, aligned to ACORD and internal Blueprint 2 standards. He will provide technical expertise in analysis, design, development, rollout and maintenance of enterprise data models and solutions. Provides technical expertise in needs identification, data modelling, data movement and transformation mapping (source to target), automation and testing strategies, translating business needs into technical solutions with adherence to established data guidelines and approaches from a business unit or project perspective. Provides data understanding and coordinate data related activities with other data management groups such as master data management, data governance and metadata management. Leadership not only in the conventional sense, but also within a team we expect people to be leaders. Candidate should elicit leadership qualities such as Innovation, Critical thinking, optimism/positivity, Communication, Time Management, Collaboration, Problem-solving, Acting Independently, Knowledge sharing and Approachable.

Essential Duties

Senior Data Architect with Snowflake & dbt knowledge who can help them with architecture towards agreed architecture.
Proven skills in enterprise & solution architecture.
Define and own the end-to-end data architecture strategy, ensuring alignment with enterprise standards, scalability requirements, and long-term business goals.
Navigates, recommends, and implements cloud-based data solutions and can work on modern tools like DBT for data transformation.
Design reference architectures and solution blueprints for cloud-based data platforms (Snowflake-centric), covering ingestion, transformation, storage, and consumption layers.
Develop Conceptual, Logical and Physical Data Models and related artifacts to specified standards.
Architect data warehouse solutions applying industry patterns — including Data Vault 2.0, dimensional modelling (Kimball), historization (SCD Type 1/2), persistent staging areas, and change data capture (CDC).
Design repeatable, industrialized ELT/ETL frameworks leveraging dbt for transformation orchestration; define integration patterns for ingesting data from heterogeneous source systems.
Establish architectural principles, design patterns, naming conventions, and data standards; conduct architecture reviews and ensure compliance across delivery teams.
Assess, recommend, and roadmap cloud-native data technologies and tooling; drive architectural decisions with documented ADRs (Architecture Decision Records).
Evaluates the business units for existing inefficiencies and bottlenecks and to develop and maintain protocols for handling, processing, and cleaning data.
Understanding of data privacy regulations (such as GDPR) and ethical considerations in handling sensitive customer data and their historization.
Ability to work as an individual contributor and effectively collaborate with Stakeholders.
Technical supervision of the project.
C360 Data model design (conceptual, logical, physical).
Define target architecture on Synapse and later migration to DBX.
Identification of source systems and integration requirements.
Model priority domains iteratively:
- Underwriting (UW) first (known/priority domain).
- Claims logical model next (claim lifecycle, reserves, payments, recoveries, parties, events).
Define target-state architecture for migration to Azure/Databricks (ingestion → transformation → serving).
Provide patterns and guardrails for:
- Batch/CDC ingestion strategies, landing zones.
- Lakehouse layering (e.g., Bronze/Silver/Gold or equivalent).
- Transformation strategy (Databricks notebooks/jobs/workflows).
- Serving patterns for reporting and downstream consumption (marts/semantic models/APIs as applicable).
Implement a data-products centric approach:
- Define domain boundaries (UW, Claims, etc.) and ownership.
- Establish data product contracts (schema, SLAs, DQ expectations, refresh frequency).
- Support federated domain teams while enforcing enterprise standards.
Establish enterprise-wide naming conventions, definition standards, and modeling patterns (keys, timestamps, code/desc, audit fields).
Build and maintain a business glossary and definitions aligned to ACORD/Blueprint 2.
Define modeling guardrails such as:
- Grain - first modeling rules.
- Identity/key strategy across domains.
- History strategy (event vs snapshot, SCD approach).
- Conformance strategy for shared dimensions/reference data.
Embed governance into design and delivery:
- Register datasets, lineage, and glossary terms in Purview.
- Enforce access controls, ownership, and data permissions via Unity Catalog.
- Ensure classification/sensitivity tagging and documentation completeness.
Understanding data integration processes (batch or real-time) using tools such as Informatica PowerCenter and/or Cloud, Microsoft SSIS, MuleSoft, DataStage, Sqoop, etc.
Create functional & technical documentation – e.g. data integration architecture documentation, data models, data dictionaries, data integration specifications, data testing plans, etc.
Define and operationalize a data quality rule catalog (completeness, validity, uniqueness, referential integrity).
Implement reconciliation and control checks between source and target.
Produce architecture and delivery artifacts:
- EDM & domain models.
- Mapping documents, DQ rule catalog, lineage, runbooks.
- Decision log and architecture standards documentation.
Collaborate with business users to analyse and test requirements.
Run domain workshops with SMEs (UW/Claims), architects, engineering, governance, and reporting teams.
Provide clear status, risks, dependencies, and decisions required.
Lead design reviews and ensure alignment with enterprise standards and delivery priorities.
Stays current with emerging and changing technologies to best recommend and implement beneficial technologies and approaches for Data Architecture.
Assist with and support setting the data architecture direction (including data movement approach, architecture/technology strategy, and any other data-related considerations to ensure business value) ensuring data architecture deliverables are developed, ensuring compliance to standards and guidelines, implementing the data architecture, and supporting technical developers at a project or business unit level.
Coordinate and consult with the project manager, client business staff, client technical staff and project developers in data architecture best practices and anything else that is data related at the project or business unit levels.

Must Have Skills & Experience

Strong hands-on expertise in enterprise and domain data modeling (conceptual/logical/physical).
Experience designing data architectures for cloud migration and modern lakehouse patterns.
Strong experience with Azure and Databricks (Delta/Lakehouse layering, orchestration design patterns).
Proven track record building guardrails: modeling standards, naming conventions, definitions/glossary.
Governance experience with Unity Catalog and Microsoft Purview (cataloging, lineage, permissions, classification).
Ability to operate effectively in a data product/domain ownership model within a broader governance program.
Strong documentation and stakeholder facilitation skills.
Insurance domain experience, especially Underwriting and Claims.
Experience delivering “first use case” accelerators for reporting migration and KPI reconciliation.
Can effectively utilise SQL and/or available BI tool to validate/elaborate business rules.
Demonstrates an understanding of EDM architectures and applies this knowledge in collaborating with the team to design effective solutions to business problems/issues.
Understands and leverages a multi-layer semantic model to ensure scalability, durability, and supportability of the analytic solution.
Understands modern data warehouse concepts (real-time, cloud, Big Data) and how to enable such capabilities from a reporting and analytic standpoint.
Demonstrated ability to serve as a trusted advisor that builds influence with client management beyond simply EDM.

Education & Experience

10-15 years of Enterprise Data Modelling.
Experience using major data modelling tools (examples: ERwin, ER/Studio, PowerDesigner, etc.).
Experience with major database platforms (e.g. Oracle, SQL Server, Teradata, etc.).
Understanding and experience with major Data Architecture philosophies (Dimensional, ODS, Data Vault, etc.).
3 - 5 years of management experience required.
3 - 5 years consulting experience preferred.
Bachelor’s degree or equivalent experience, Master’s Degree Preferred.
Experience in data analysis and profiling.
Strong data warehousing and OLTP systems from a modelling and integration perspective.
Strong understanding of data integration best practices and concepts.
Strong development experience under Unix and/or Windows environments.
Strong SQL skills required scripting (e.g., PL/SQL) preferred.
Strong Knowledge of all phases of the system development life cycle.
Understanding of modern data warehouse capabilities and technologies such as real-time, cloud, Big Data.
Understanding of on premises and cloud infrastructure architectures (e.g. Azure, AWS, Google Cloud).