Senior Data Analyst / Analytics Engineer

TO APPLY, ALL APPLICANTS MUST SUBMIT RESUME TO HTTPS://WWW.JOBPOSTINGTODAY.COM/ AND REFERENCE ID#39782 Digital Turbine, Inc. dba Digital Turbine seeks Senior Data Analyst / Analytics Engineer in New York, NY: Design and execute statistical experiments, build production data pipelines, develop analytical frameworks, and deliver data-driven insights that support revenue optimization and campaign performance across a global mobile advertising technology platform. Work closely with commercial teams as well as product & engineering, to figure out ways to acquire new users, retain existing users, and optimize the all-around user experience for our customers. Build a strong knowledge of company databases, key data points, and reporting styles of commercial teams whilst learning how data drives critical business decisions. Responsible for continuing to instill a strong data culture amongst commercial teams. Stakeholder Partnership: Partner closely with commercial and product teams to define and implement business metrics and KPIs, and deliver actionable analyses. Brainstorm ways to increase growth and drive the business revenue. Self-Serve Analytics: Build high-impact dashboards and analytics tools to increase visibility and enable stakeholders to self-serve their own data requirements effectively. Own the design, development, validation, and maintenance of ongoing metrics, dashboards, and analyses to drive key business decisions. Experimentation & Causal Inference: Design and execute randomized controlled experiments (A/B tests) to measure the causal impact of product features on revenue KPIs; apply statistical hypothesis testing (Z-tests, proportion tests, rate comparisons) to determine significance; build annualized revenue impact models to project experimental results to full-year business outcomes. Data Science & Behavioral Modeling: Apply data science techniques to large-scale behavioral datasets, including user segmentation, clustering, co-occurrence analysis, temporal pattern discovery, and geographic segmentation, to inform campaign targeting strategy and optimize engagement. Analytics Engineering (dbt): Design and maintain a dbt transformation layer with layered model architecture (staging/intermediate/core), reusable macros, automated tests, and optimized materialization strategies that reduce query runtimes and enforce data quality standards. Advanced SQL & Query Optimization: Write and optimize complex analytical SQL against large-scale partitioned datasets for cohort-based funnel attribution and revenue analysis, reconciling cross-system identity semantics across multiple source systems. Data Quality & Auditing: Conduct systematic data quality investigations, analyzing data to identify deliverables, gaps, and inconsistencies. Trace data lineage issues across multiple system boundaries, quantify revenue impact, and implement fixes validated through multi-dimensional KPI comparison. Audience Targeting: Build precision-targeted device audiences from large populations by composing multi-layered filtering criteria (behavioral, geographic, recency, eligibility) to produce high-engagement segments for partner notification campaigns. Dashboard Development: Develop and maintain Tableau datasources and dashboards serving as the analytical source-of-truth for executive and partner-facing reporting, including datasource migrations with backward compatibility across connected workbooks. Software Engineering (Python): Design and maintain reusable Python/PySpark libraries with object-oriented design patterns, aggregation frameworks, cohort funnel functions with data validation, and shared analytical components used across the team's notebooks and workflows. AI & Cursor Integration: Build AI-augmented analytics workflows integrating large language models with enterprise data platforms to automate code review, validation pipelines, and institutional knowledge management. Leverage AI-powered coding environments like Cursor AI