Business Data Analyst

Role Overview

We are looking for a Technical Data Architect who can design data solutions, understand complex data landscapes, and translate business requirements into scalable Data Products and Data Architecture.

The candidate will work with technologies such as Snowflake, Data Vault, Immuta, Collibra and Cloud Security, while collaborating closely with business stakeholders, data engineers and analytics teams.

Must-Have Skills

1. Data Architecture – Mandatory

• Data & Analytics Architecture
• Data Modeling
• Data Mesh concepts
• Data Products
• Information Architecture

1. Snowflake & Data Vault – Mandatory

• Strong Snowflake experience.
• Good understanding of Data Vault concepts and design.
• Ability to translate an existing/legacy data landscape into a Data Vault architecture.
• Look for: Snowflake, Data Vault 2.0, Data Vault Modeling, Data Vault Architecture.

1. Data Engineering – Mandatory

• Experience as a Data Engineer / Data Architect.
• Data ingestion and transformation.
• Data pipelines.
• Data quality and reliability.
• SQL and preferably Python.

1. Business Data Analytics – Mandatory

• Client specifically mentioned Business Data Analytics.
• Experience understanding business data, analytics requirements and converting them into data products/solutions.

1. Salesforce – Required

• Experience working with Salesforce data / Salesforce analytics.
• Understanding of Salesforce data structures and how Salesforce data integrates with enterprise data platforms is valuable.

1. Tableau – Required

• Experience with Tableau for reporting, dashboards or analytics.
• Candidate should understand how data is prepared and consumed by BI/reporting platforms.

Good-to-Have Skills

• Immuta
• Collibra
• Cloud Security
• GitLab / CI/CD
• APIs and data extracts
• Data sharing agreements
• Data SLAs
• Data observability
• Data quality frameworks
• Agile / Scrum

Key Responsibilities

• Understand the existing/legacy data architecture and design future-state solutions on Snowflake/Data One Platform.
• Convert business requirements into Data Product blueprints.
• Design data models and data pipelines.
• Work with senior business stakeholders to understand requirements and recommend architecture options.
• Guide Data Engineers/developers to implement the architecture correctly.
• Ensure data quality, reliability and observability.
• Work with analytics/reporting teams to ensure data products support business insights.
• Review and improve existing architecture and data engineering practices.
• Act as a quality gatekeeper for developed Data Products.