Design, build, and maintain analytics‑ready data models, including facts, dimensions, and data marts.
Develop and manage transformations primarily in the semantic and transformation layer using SQL‑based tooling (e.g., dbt).
Define and maintain metrics, business logic, and calculations to ensure consistency across dashboards, reports, and analyses.
Apply dimensional modeling best practices (e.g., star schemas) optimized for BI and analytical consumption.
Partner closely with product, operations, finance, and business stakeholders to understand requirements and translate them into well‑defined data models and metrics.
Act as a steward of business logic—ensuring definitions are clear, documented, and aligned across teams.
Proactively identify gaps, ambiguities, or inconsistencies in metrics and drive alignment toward standardized definitions.
Optimize data models for clarity, usability, and performance for downstream consumers, including analysts and self‑service BI users.
Support analysts and BI developers by enabling faster, more reliable dashboard and report development.
Ensure analytics outputs are intuitive, discoverable, and trusted by decision‑makers.
Implement data quality checks and testing at the analytics layer to ensure accuracy and reliability.
Contribute to analytics engineering standards, conventions, and documentation.
Collaborate with data engineering partners to provide feedback on source data structure and readiness for analytics.

Bachelor’s degree in Computer Science, Data Analytics, Statistics, Mathematics, or a related field (or equivalent experience)
5+ years of experience in analytics engineering, analytics, business intelligence, or a related role.
Advanced SQL expertise, with a strong track record of building complex, maintainable transformations.
Hands‑on experience with analytics engineering tools (e.g., dbt or similar transformation frameworks).
Strong understanding of dimensional modeling, metrics design, and semantic layer concepts.
Experience supporting BI tools and downstream analytics use cases (dashboards, reporting, ad hoc analysis).
Ability to communicate clearly with both technical and non‑technical stakeholders.

Senior Analytics Engineer

Key skills