Remote Snowflake Data Modeler — Pharma Data Platform

Fusion Consulting; Lisboa; Remote; EUR 60,000 - 80,000; Full time

Benefits offered by this job

Remote work up to 25 days/year

Flexible time off

Integration budget for team activities

Birthday gift

Professional development budget

Job summary

Fusion Consulting is seeking a Data Modeler (Snowflake) to join our Life Sciences data team. You will design Snowflake physical data models, translate concepts into schemas, tables and views, and collaborate with data engineers and governance teams on scalable, maintainable solutions.

You’ll document modeling logic for traceability, align with governance (Collibra), and support ETL/ELT processes. This mid‑level role is remote‑friendly, globally distributed, and offers growth across data and

Qualifications

• 4–7 years of experience as a Data Modeler or similar role.
• Proven Snowflake physical data modeling and SQL expertise.
• Experience with metadata and governance platforms (Collibra).
• Familiarity with AWS services, especially S3, is a plus.
• Strong documentation and communication skills.

Responsibilities

• Design and implement Snowflake physical data models.
• Translate conceptual models into Snowflake schemas, tables and views.
• Collaborate with data engineers on ETL/ELT patterns.
• Align models and metadata with governance standards (Collibra).
• Ensure documentation for traceability and compliance.

Skills

Snowflake

SQL

Data modeling

ETL/ELT

Collibra governance

dbt

AWS S3

Tools

dbt

Matillion

Fivetran

Job description

Fusion Consulting is seeking a Data Modeler (Snowflake) to join our Life Sciences data team. You will design Snowflake physical data models, translate concepts into schemas, tables and views, and collaborate with data engineers and governance teams on scalable, maintainable solutions.

You’ll document modeling logic for traceability, align with governance (Collibra), and support ETL/ELT processes. This mid‑level role is remote‑friendly, globally distributed, and offers growth across data and