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