Data Modeler

We are looking for an experienced and motivated Data Modeler – Collibra & Python to support enterprise data modelling, metadata management, and data governance initiatives within a complex financial-services environment.

The successful candidate will combine strong Python development skills with hands‑on experience in Collibra, metadata modelling, data governance, data lineage, data quality, and regulatory data management.

As a Sr. Data Modeler Engineer, you will design, enhance, and maintain enterprise asset models and governance solutions while developing reusable Python services and automation components to support data ingestion, governance workflows, integrations, validation, lineage, and reporting.

Design, enhance, and maintain asset models in Collibra aligned with enterprise data governance, metadata management, regulatory, and technology-risk requirements.

Define and configure Collibra asset types, relations, attributes, communities, domains, responsibilities, workflows, roles, and operating models.

Develop high-quality Python solutions for data modelling and governance use cases.

Use Pydantic for schema definition, validation, serialization, configuration-driven models, and API-ready data structures.

Develop reusable Python libraries, modelling components, and automation patterns for data ingestion, governance workflows, data quality, lineage, integrations, and reporting.

Design solutions to process and govern large-scale financial and risk data sets, with a focus on performance, data integrity, traceability, and regulatory controls.

Build and support integrations using APIs, JSON, YAML, SQL, and structured or unstructured data formats.

Use in-memory databases or caching technologies to support performant integrations, validation workflows, and governance automation.

Support Collibra APIs, workflow development, import/export tooling, Edge/Connect integrations, and related integration patterns.

Collaborate with data architects, engineers, data stewards, business stakeholders, risk teams, compliance teams, and technology-control functions.

Use AI-assisted development tools responsibly to improve development productivity, testing, documentation, and solution design.

Review all AI-generated outputs for security, compliance, accuracy, maintainability, and production readiness.

Maintain technical documentation, data models, governance procedures, code documentation, and operating guidelines.

Bachelor’s degree in Computer Science, Information Technology, Data Management, Engineering, or a related discipline.

Strong professional experience in Python development for data modelling, governance, data engineering, or enterprise integration use cases.

Hands‑on experience with Collibra or a comparable metadata management and data governance platform.

Experience designing and configuring Collibra asset models, meta models, workflows, roles, responsibilities, domains, communities, and operating models.

Strong knowledge of metadata modelling, logical data modelling, data taxonomies, data lineage, data quality, business glossaries, data catalogues, critical data elements, and governance workflows.

Practical experience with Pydantic for schema definition, validation, serialization, and configuration-driven models.

Experience with FastAPI, REST APIs, JSON, YAML, SQL, and service integration.

Experience building reusable Python libraries, automation tools, governance workflows, and data integration components.

Understanding of data architecture, enterprise information management, regulated data life cycles, and modern engineering practices.

Experience with Git-based development, code review, automated testing, documentation, and CI/CD concepts.

Strong…