Growth Data Analyst

Responsibilities

• Design, create, and uphold interactive dashboards within Tableau to cater to recurring and ad-hoc reporting requirements across different growth departments and managerial teams, enabling real-time tracking of performance and insights.
• Craft and enhance complex SQL queries on Databricks to propel business growth in user acquisition, campaign return on investment (ROI), and management of customer lifecycle.
• Collaborate with various teams—like growth, product, data engineering, and external partners—to enhance data infrastructure, independently resolve pipeline challenges, and ensure precise, scalable, and competent data pipelines that back business objectives.
• Simplify and automate regular data workflows and procedures, supervise SQL automation and job scheduling, and sustain detailed documentation to advance team efficiency and data dependability.
• Concoct analytical models to direct marketing strategies, such as predictive lifetime value (LTV), lead scoring or marketing mix modeling, presenting actionable insights for campaign preparation and enhancement.
• Employ statistical techniques and business intelligence tools to unveil patterns, trends, and opportunities that guide strategic growth choices.
• Work closely with multi-departmental partners to implement data-influenced resolutions and resource end-to-end project delivery, ensuring synchronization with business aims and timelines.
• Progress professionally by staying updated on developing AI tools and practices within data science and marketing analytics, consequently boosting analytical skills and industry insights.

Requirements

• A degree in a quantifiable field like Computer Science, Statistics, Engineering, Information Systems, or linked disciplines.
• 2+ years of practice in data analysis or a parallel domain. Familiarity with the Crypto and Technology sector is advantageous.
• Skills in SQL, Databricks, and Tableau for processing, studying, and visualizing extensive datasets.
• Proficiency in statistical tools (e.g., R, Python) and libraries for control, adaptation, and analysis of data.
• Prowess in analysis with the capability to aggregate, systematize, analyze, and distribute substantial data with precision and detail.
• Proficient in querying, report composition, and presenting discoveries.
• Understanding of digital marketing ideas, like various user acquisition methods (organic, non-organic, alliances, etc.), campaign control, and management of customer lifecycle.
• Acquaintance with tools such as AppsFlyer, Google Tag Manager, Google Analytics, and SensorTower.
• Strong communication proficiency to deliver complex data insights understandably to non-technical colleagues and reframe business needs into technical and data necessities.
• Capability to thrive in a dynamic environment, monitor numerous projects, and adapt to fluctuating priorities.