Analytics Engineer

Our client, a car insurance company, is now looking for an Analytics Engineer to take ownership of analytics, reporting, and BI dashboards for a growing AI-driven team within a leading technology organization.The team produces high volumes of production data, including model predictions, chatbot interactions, and operational automation. Currently, there is a need for a dedicated analyst to monitor, analyze, and visualize this data to provide actionable insights across the organization.ResponsibilitiesDashboard ownership (primary focus): Design, build, and maintain Looker dashboards that track ML model performance (across approximately 12 production models) Own the "what to show" decision for each dashboard — choose appropriate metrics, time windows, and granularity independently Translate model performance data into business terms for stakeholders — enabling decisions like "is it time to retrain?" or "is there a feature data problem?" rather than reporting raw statistical outputsData Modeling And Transformation Extend and maintain existing DBT incremental models that join predictions to actuals with time-lag offsets (e.g., predictions from day T joined to actuals from day T+15) Apply DBT materialization strategies (incremental, table, view) and manage full refreshes after schema or logic changesMonitoring And Collaboration Design alerting logic to flag model degradation or abnormal prediction patterns Collaborate with ML engineers to understand each model's prediction logic and define what healthy performance looks like Respond to ad-hoc analytical questions from ML engineers, product, and leadershipRequirementsCore Requirements: 5+ years in analytics engineering, BI development, or data analytics Experience with Looker, LookML Production experience with DBT (Data Build Tool) — incremental models, materialization strategies, DAG dependencies Experience with Snowflake or equivalent cloud data warehouse (BigQuery, Redshift) Experience with SQL (production-level, complex analytical queries across large datasets) Demonstrated ability to design and build performance or operational dashboards — not just business KPI reports Experience with data modeling — fact tables, dimensional models, time-windowed joins Strong communication skills with experience presenting analytical findings to non-technical stakeholders Experience with Airflow or similar orchestration tools Experience with Git/version control Ability to work independently and proactively identify data quality or reporting issues Familiarity with ML model evaluation concepts — understanding how to assess whether a predictive model is performing well and when performance is degradingPreferred Qualifications Experience with model monitoring concepts (data drift, feature drift, concept drift) Insurance or fintech domain experience (conversion funnels, policy lifecycle, claims) Experience working embedded within AI/ML engineering teams Python for ad-hoc analysis and automation