Analytics Engineer
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Job Description
Analytics Engineer building Gold layer and dbt Semantic Layer for a tech consulting firm. Engage directly with clients for metric validation and operational support.
Responsibilities:
- Build Gold layer dbt models, including metric-level aggregations, KPI tables, and denormalized summary tables optimized for BI consumption in Sigma.
- Author dbt Semantic Layer metric definitions using MetricFlow, including sell-through, out-of-stock (OOS) rate, conversion rate, average order value (AOV), weeks cover, and other metrics defined in the business metric register.
- Populate and maintain the business glossary for each completed dataset, including metric definitions, calculation notes, known caveats, ownership, and data lineage.
- Participate in business validation sessions with the onshore team and business stakeholders to ensure Gold metrics align with agreed metric register definitions prior to handover to Run operations.
- Author and maintain dbt Explorer documentation, including model descriptions, column descriptions, and metric definitions to ensure the semantic layer remains fully self-documenting.
- Support ongoing Run operations by managing metric change requests, implementing new Gold model additions, monitoring Cortex ML models, maintaining Claude NLQ context updates, and tracking AI agent uptime and performance.
- Collaborate with stakeholders and cross-functional teams to validate business requirements, clarify KPI logic, and ensure consistent metric governance across the platform.
Requirements:
- Advanced experience with dbt Semantic Layer and MetricFlow, including metric definitions, dimensions, entities, measures, and time spines
- Advanced SQL expertise, including metric logic, window functions, ratio metrics, and period-over-period comparisons
- Basic Python scripting skills for automation and monitoring tasks
- Intermediate experience with Power BI / MS Fabric (or similar BI tools), including DAX, paginated reports, RLS/RBAC, performance tuning, large dataset handling, and validation of Gold layer outputs
- Intermediate experience with Atlan or similar data catalog tools, including glossary management, catalogue administration, and lineage review
- Strong retail domain knowledge, including key metrics such as sell-through, OOS rate, clearance %, return rate, and AOV
- Strong communication and stakeholder management skills, with the ability to clearly present metric validation findings to both technical and non-technical stakeholders
- Experience using Claude API or similar AI tooling to translate business metric register rules into dbt Gold model SQL.
- Familiarity with Cortex Analyst YAML generation for scaffolding Semantic Layer definitions aligned with metric register requirements.
- Experience monitoring Cortex ML models, including Forecasting and Anomaly Detection performance, and triggering retraining workflows when necessary.
- Understanding of NLQ (Natural Language Query) context management, including maintaining governance and semantic layer context that supports AI-powered business user interfaces.
- Exposure to LLM APIs, Snowflake Cortex, or similar AI-driven analytics platforms, whether in production, prototype, or personal project environments.
- Manage metric change requests by updating Gold models and Semantic Layer definitions as business rules and KPI definitions evolve.
- Monitor AI agent performance, including NLQ answer quality, identifying model drift, escalating issues to the lead engineer, and updating AI system prompt context as new datasets are introduced.
- Oversee Cortex ML maintenance by monitoring forecast accuracy and triggering retraining processes when performance falls below agreed thresholds.
- Build and maintain new Gold model additions as the organization expands the Gold layer and introduces additional business metrics.
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