Important Information
- Experience: +6 years
- Job Mode: Full-time
- Work Mode: Work from home
Responsibilities and Duties
- Build and maintain layered analytical models across device lifecycle, app usage, and monetization (RPH, ARPU, eCPM, fill-rate, retention).
- Reconcile data from multiple operational, product, and revenue systems; surface and track discrepancies.
- Create entity-centric models for health, attribution, and longitudinal behavior.
- Analyze auction funnels end-to-end (requests → bids → wins → impressions → revenue) and diagnose drop-offs.
- Monitor core KPIs with anomaly detection and investigation playbooks to accelerate root-cause analysis.
- Develop cohorting, time-series, and survival analyses for growth, churn, and LTV.
- Implement anomaly detection and alerting on core revenue/usage KPIs.
- Design scalable KPI dashboards in Apache Superset (daily/weekly/monthly) for executives and operators; enable self-serve exploration.
- Establish a governed metrics layer/semantic definitions to keep “one source of truth” across teams.
- Use templating/parameterization to make dashboards reusable by region, app, device class, cohort, etc.
- Align on schemas, SLAs, and data contracts; improve pipeline reliability and query performance.
- Build performant rollups/aggregate tables (daily/weekly/hourly) and materialized views for high-traffic queries; set sensible refresh cadences.
- Add tests, documentation, and lineage so models are trustworthy and maintainable.
Qualifications and Skills
- 6+ years in BI/Data Analytics (media/CTV/streaming or ad-supported required), owning high-impact dashboards and analyses.
- Expert SQL on modern warehouses (Redshift/Snowflake/BigQuery); comfortable with multi-CTE patterns, window functions, tuning, and semi-structured JSON/SUPER parsing.
- Strong BI chops (Superset/Tableau/Looker/Mode) with an eye for clarity, performance, and reuse. We currently use Superset for our visualizations.
- Fluency in monetization metrics (ARPU, CPM/eCPM, RPH, fill-rate, auction stages) and attribution nuances.
- Familiarity with streaming/app lifecycle data (sessions, playback, retention, cohorts) and high-volume event streams.
- Excellent communicator: crisp problem framing, clear data stories, and pragmatic recommendations.
- Program/project mindset: prioritization, stakeholder alignment, on-time delivery.
Nice to Have
- Experience using and setting up an “ask-the-data” experience with natural language AI tools, helping stakeholders get quick, reliable insights without deep SQL knowledge in a self-service manner.
- Python/R for analytics (pandas, NumPy), notebooks, and lightweight ETL.
- dbt for model governance; Airflow/Prefect for orchestration; Git-based workflows and CI for analytics.
- Experience with ad tech & measurement (OpenRTB, SSAI, MMPs, household graphs, privacy/consent frameworks).
- Time-series forecasting, anomaly detection, and causal inference methods.
- Metric layer tools (e.g., dbt Semantic Layer, LookML, Cube/MetricFlow) and documentation/lineage tools.
- Spark/PySpark for scale; familiarity with cost/perf tuning in cloud data stacks.
