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.
Responsibilities:
Dashboard 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 outputs
Data 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 changes
Monitoring 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 leadership
Requirements:
Core 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 degrading
Preferred 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
