Lead Consultant(Development)

Job Summary

Data Modeller

Key Responsibilities

• Assist with Schema Design: Help build, update, and maintain logical and physical data models. Any domain.
• Translate Legacy Logic: Analyze existing legacy SQL queries and help map them into clean, structured dimensional models in the cloud.
• Support the Canonical & Semantic Layers: Collaborate with Analytics Engineers to document and define columns, keys, and metrics within the shared transformation layer.
• Maintain Data Contracts: Update YAML or schema files to ensure data products include required metadata, such as ownership, schema definitions, and automated test rules.
• Document Lineage & Catalogue: Map end-to-end data lineage from source systems to final dashboards, and ensure all entries are kept up-to-date in the data product catalogue.
• Apply DQ Checks: Integrate standard data quality tests (e.g., null checks, unique constraints, and data type validations) directly into the model definitions.

Required Skills & Experience

• Core Data Modelling: 2–5 years of experience in data modelling, with a solid understanding of relational databases and Kimball dimensional modelling (Stars and Snowflakes).
• Strong SQL: Advanced SQL skills with the ability to read, optimize, and reverse-engineer complex legacy queries.
• Modern Cloud Exposure: Hands-on experience or strong working knowledge of cloud platforms like Snowflake or AWS S3/Lakehouses. Knowledge of Apache Iceberg is a strong plus.
• Financial Services Exposure: Prior experience in banking, wealth management, or financial services is highly desirable.
• Tooling Familiarity: Experience using data modelling tools (e.g., Erwin, Hackolade, or dbt) and version control systems (Git).
• Eagerness to Learn: A proactive attitude and desire to grow your skills across modern analytics engineering, data contracts, and data streaming (Kafka).

Key Responsibilities

Key Responsibilities

• Assist with Schema Design: Help build, update, and maintain logical and physical data models. Any domain.
• Translate Legacy Logic: Analyze existing legacy SQL queries and help map them into clean, structured dimensional models in the cloud.
• Support the Canonical & Semantic Layers: Collaborate with Analytics Engineers to document and define columns, keys, and metrics within the shared transformation layer.
• Maintain Data Contracts: Update YAML or schema files to ensure data products include required metadata, such as ownership, schema definitions, and automated test rules.
• Document Lineage & Catalogue: Map end-to-end data lineage from source systems to final dashboards, and ensure all entries are kept up-to-date in the data product catalogue.
• Apply DQ Checks: Integrate standard data quality tests (e.g., null checks, unique constraints, and data type validations) directly into the model definitions.

Skill Requirements

Required Skills & Experience

• Core Data Modelling: 2–5 years of experience in data modelling, with a solid understanding of relational databases and Kimball dimensional modelling (Stars and Snowflakes).
• Strong SQL: Advanced SQL skills with the ability to read, optimize, and reverse-engineer complex legacy queries.
• Modern Cloud Exposure: Hands-on experience or strong working knowledge of cloud platforms like Snowflake or AWS S3/Lakehouses. Knowledge of Apache Iceberg is a strong plus.
• Financial Services Exposure: Prior experience in banking, wealth management, or financial services is highly desirable.
• Tooling Familiarity: Experience using data modelling tools (e.g., Erwin, Hackolade, or dbt) and version control systems (Git).
• Eagerness to Learn: A proactive attitude and desire to grow your skills across modern analytics engineering, data contracts, and data streaming (Kafka).

Other Requirements

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