Data Analytics Director

As a Data Analytics Director at Jellyfish, your primary responsibilities will include turning complex data into clear insights that drive media strategies, designing measurement frameworks, leading client relationships including multi-market pitches, overseeing media reporting for actionable insights, championing data automation and AI, and guiding holistic media planning through media mix models. Each role emphasizes collaboration and innovation in the fast-paced digital landscape.

To qualify for the Data Analytics Director role at Jellyfish, candidates should possess at least 5 years of experience in data analytics or business intelligence, particularly with Paid Media data. Leadership experience is required, alongside fluency in digital media platform data and reporting. Proficiency in data automation tools such as SQL or Python and data visualization tools like Tableau or Power BI is also essential. Additionally, candidates should demonstrate the capability to build measurement frameworks and collaborate effectively with diverse team members.

At Jellyfish, we prioritize growth and provide numerous opportunities for professional development for Data Analytics Directors. Our unique benefits include one paid day each month dedicated to self-development and access to unlimited online courses via LinkedIn Learning. We value continuous learning and provide a supportive culture that encourages innovative thinking and improves your skill set.

The team culture at Jellyfish is deeply rooted in collaboration, diversity, and inclusion. As a Data Analytics Director, you will work alongside creative minds, tech enthusiasts, and data experts, all committed to empowering clients. We celebrate unique contributions and encourage team members to express their ideas, pushing the boundaries of traditional marketing practices to achieve extraordinary results together.

Jellyfish offers a flexible work environment for the Data Analytics Director role, allowing you to work remotely for up to 60% of your days. You have the freedom to customize your work hours between 8 AM and 6:30 PM, promoting a healthy work-life balance that suits your personal and professional needs.

When answering this question, highlight specific examples of how you've transformed complex data into actionable insights. Describe the tools you used and the impact of your data storytelling on client decision-making and marketing strategies.

In your response, mention the methodologies or frameworks you employ, such as SMART or logic models. Explain how your approaches have successfully measured marketing performance, and provide examples of past projects where you've seen measurable results.

Discuss your strategies for building trust and rapport with clients, highlighting communication, transparency, and understanding their needs. Provide examples of long-term client relationships you've fostered and how they benefited both parties.

Be prepared to share your experience with specific tools like SQL, Python, or any others you’re proficient in. Explain why you prefer these tools, including their effectiveness in data processing, level of automation, and ease of use in generating insights.

Share a specific project where you were the lead, detailing the challenges you faced, your approach to overcoming them, and the successful outcomes achieved. Be sure to quantify your impact, such as improvements in efficiency or client satisfaction.

Discuss your leadership style, focusing on how you motivate and support team members' development. Provide examples of mentorship you've provided and the positive outcomes that have resulted from fostering a collaborative team environment.

Explain your strategies for continuous learning, such as attending webinars, participating in professional networks, and following industry publications. Mention specific resources you find valuable for staying current with changes in data analytics and tech.

Respond by sharing your experiences in implementing media mix models and how they influence your cross-channel strategy decisions. Include specifics about the outcomes and learnings from previous campaigns.

Provide examples of difficulties you've encountered in data visualization and your creative solutions. Illustrate how your approaches have led to improved clarity and insights for stakeholders and clients.

Discuss the key performance indicators (KPIs) you prioritize and how you analyze data to assess campaign performance. Share how your assessments lead to actionable insights for clients and improve future campaigns.