Market Data Analyst

Formamos parte de una organización dinámica y flexible con gran capacidad de adaptación y compromiso. Valoramos las competencias, habilidades, experiencia y potencial de las personas, así como sus cualidades personales, aplicando dichos valores tanto a nuestro trato con los clientes como entre nosotros.

Role Summary : Synthesize internal performance data (attendance, revenue, loyalty) with external market intelligence to guide programming, pricing, and marketing. Embed data quality and testing into all market analyses.

Key Responsibilities :

• Market Analysis : Analyze market and competitor data (e.g. box office trends, genre performance, pricing, external events) to inform decision-making. Build interactive Power BI or Sigma reports that compare our titles / offers with industry benchmarks. Forecast impacts of pricing changes or seasonality using scenario modeling. As reported for market analysts, key tasks include monitoring and forecasting trends, and converting data into actionable reports and visualizations.
• Data Integration & QA : Ingest and reconcile external market feeds (e.g. syndicated box office data, weather impacts, ticketing APIs) with internal series data. Build automated quality checks (variance alerts, content validation) to ensure feed integrity. Define tolerances for external data (e.g. expected ranges) and alert on anomalies.
• Campaign Analytics : Partner with marketing / Audience Ops to evaluate promotional campaigns by segment. Audit conversion tracking, ensure UTMs and pixels are firing correctly for market channels. Attribute campaign impact accurately in BI dashboards.
• Pricing & Scenario Modeling : Develop tools (in Power BI / Sigma) to model pricing strategies, showtime allocations, and offer sensitivities. These models should have auditable inputs and clear assumptions. Enable stakeholders to simulate “what-if” scenarios (e.g. price elasticity) with confidence.
• Insights Delivery : Publish regular (e.g. weekly) market-performance reports that highlight trends, opportunities, and risks. Include methodology and QA notes to ensure transparency. Provide reproducible analysis artifacts (SQL queries, Python notebooks) alongside insights for auditability.
• Documentation & Coordination : Document all analysis processes, data sources, and assumptions in Confluence or an internal data catalog. Create SOPs (via Scribe or wiki) for running reports and pipelines. Work across teams (Programming, Marketing, Data Platform) to align on data definitions and guardrails.

Core Technical Skills :