Data Modelling Engineer (Databricks)

Location: Singapore; flexible hybrid work environment

Compensation: Competitive pay and bonus eligibility

Company

hirify.global provides a data network and intelligence architecture for capital markets, helping institutional investors, asset managers, and service providers transform fragmented data into decision-grade intelligence.

What you will do

• Design and maintain scalable conceptual, logical, and physical data models on Databricks using Delta Lake and Lakehouse architecture.
• Profile index, pricing, reference, ESG, and research datasets to assess structures, fields, data types, keys, and vendor-specific characteristics.
• Identify data inconsistencies, missing fields, formatting issues, and schema gaps, and collaborate on remediation.
• Define source-to-target mappings, schemas, and handling rules for ETL/ELT implementation, working with Data Engineering to ensure accurate builds.
• Test sample data against models, use Unity Catalog for governance and lineage, and adjust models for provider schema updates.
• Document field definitions, relationships, and handling rules while contributing to modelling standards and data quality practices.

Requirements

• 5+ years of experience in data modelling, including designing conceptual, logical, and physical models for production platforms.
• Strong understanding of relational and dimensional modelling, including hierarchies and history handling such as slowly changing dimensions.
• Strong SQL skills and hands-on experience profiling and validating unfamiliar datasets.
• Hands-on experience with Databricks, including Delta Lake and Unity Catalog.
• Python experience for data profiling and validation, plus experience with Git or similar version control systems.
• Degree in Engineering, Computer Science, Mathematics, or another technical discipline, or equivalent experience demonstrating strong analytical and data modelling capability.
• Strong attention to detail, independent dataset investigation skills, clear communication, and comfort working with cross-functional teams.
• Organized, proactive, and committed to accuracy and continuous improvement.
• Experience with financial data, including research datasets such as ESG data, and the ability to model data for analytical and investment use cases.

Nice to have

• PySpark experience for data exploration, prototyping, or transformation development.
• Familiarity with XML, JSON, and other common data exchange and schema formats.
• Experience with modern data platforms such as Palantir Foundry or Snowflake.
• Strong Excel skills for exploratory data assessment.
• Progress toward the CFA or a similar investment qualification, or equivalent knowledge of investment data and concepts.

Culture & Benefits

• Flexible hybrid work environment.
• Private health insurance for eligible employees and dependents.
• 23 days of annual leave.
• Employee Assistance Programme.
• Commitment to diversity, inclusion, career progression, training, and development.