Associate Data Analyst

Responsibilities:

• Provide insight-driven recommendations to optimize segment strategies, enhance operational process and enrich contact database components.
• Draw on their domain knowledge to help business leaders identify and prioritize their business problems.
• Recognize demand-gen and sales patterns across market segments vs performance targets.
• Support analytic tagging to track, measure, and report the effectiveness of all demand-gen and sales activities to senior managers.
• Leverage report generation software tools such as HubSpot Marketing Analytics, Microsoft Power BI, and Microsoft Excel. Also, data visualization applications like Tableau.
• Collaborate with internal and external partners to ensure newly acquired and existing geographic, demographic, and psychographic contact information is accessible for use by marketing and sales.
• Leverage data management tools like MySQL, IBM DB2, SQL Lite, and Oracle RDBMS, among others to develop & deliver marketing & sales list on a timely cadence.
• Help develop and implement best-in-class customer-centric campaign strategies.
• Strong Collaboration, acting as an internal consultant to campaign strategist and requirements.
• Documenting processes and complying with best practices in database management.

Qualifications:

• 2 years of experience managing a large database: Acquiring lists and database enrichment, and Intent data experience is also a must.
• A minimum of 2 years recent SaaS-based experience required.
• Strong working knowledge base in quantitative and analytical techniques including report analysis, association rules, decision trees, timeline and task plan analysis, and visualization techniques.
• Experience driving sophisticated exploratory data analysis and providing results to key stakeholders.
• Hands-on work building reporting automation between CRM and other database platforms.
• Excellent verbal and written communication skills.
• Excellent organizational skills and attention to detail.

Key Skills:

• Advanced Microsoft Excel.
• R or Python – Statistical Programming.
• Data visualization.
• Data modeling.
• Data cleaning and preparation.
• Machine learning.
• Critical thinking.