Senior Data Analytics Engineer ($50-75k/m + bonus)

The company is a leading financial institution and market leader in its sector. They are expanding the team to support the use of AI and ML into business.

Reponsibilities

• Build clean, modular, and maintainable data models (e.g., dimensional/star schema) that standardize core business entities and ensure consistency across all analytical use cases.
• Work closely with stakeholders to define, align, and document key metrics, ensuring consistent calculations, definitions, and interpretation across teams and tools.
• Create curated datasets and semantic abstractions that simplify complex data structures, making them easily consumable by BI tools and non-technical users.
• Enable business users through well-designed dashboards, data models, and reporting layers that prioritize usability, clarity, and performance.
• Implement robust testing frameworks, data validation checks, and monitoring processes to ensure accuracy, reliability, and trust in all analytics outputs.
• Partner with data engineers to ensure reliable, timely data ingestion and transformation pipelines, while owning the downstream analytics data layer.
• Structure datasets, metadata, and schemas in a way that supports future integration with LLM-driven querying, ensuring interpretability and semantic clarity.
• Develop and maintain clear documentation for data models, metrics, lineage, and usage guidelines to drive adoption and trust across the organization.

Requirements

• Master's or PhD in Data Science, Computer Science, Statistics, Engineering, or related field
• 6-8 years of experience in data analytics, analytics engineering, data science, or similar roles
• Strong expertise in SQL and data modeling (e.g., dimensional modeling, star schema)
• Experience with modern data transformation tools (e.g., dbt or equivalent)
• Hands-on experience with BI tools (e.g., Power BI, Tableau, Looker)
• Familiarity with data warehouses (e.g., Snowflake, BigQuery, Redshift)
• Proficiency in Python for data processing or prototyping
• Strong understanding of business metrics and self-service analytics design
• Exposure to LLM / AI-driven analytics concepts