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The Role:The Marketing Data Analysis Manager will direct a team responsible for evaluating marketing use cases and conducting comprehensive reviews of current data sources and approaches. This role is for a senior data expert who will own the relationship with key client stakeholders and ensure the organization leverages optimal data assets to activate key use cases and is able to articulate the incremental business value these use cases will deliver to the business.
Core Skills:
Data Analysis & Interpretation: Ability to analyze large, complex datasets and derive actionable insights.
Marketing Acumen: Deep understanding of marketing strategies, campaign measurement, and attribution models.
Technical Proficiency: Familiarity with SQL, data visualization tools (Tableau, Power BI), and cloud-based data solutions (Azure preferred).
Communication & Storytelling: Skilled at presenting complex data findings to non-technical audiences.
Leadership & Collaboration: Strong team management and cross-functional coordination skills.
Governance & Compliance: Knowledge of data governance principles and best practices.
Key Responsibilities:
Lead Use Case Assessment
Lead the assessment of priority marketing use cases, collaborating with stakeholders to document findings, expected outcomes, and define impact.
Analyze data needs, feasibility, and potential impact for each use case.
Quantify the incremental value and business impact that each additional use case could deliver.
Data Review, Source Identification & Requirements
Oversee detailed assessments of existing data strategies and architectures related to priority use cases.
Identify, validate, and document the most relevant data sources (internal and external) for each use case.
Evaluate the breadth and depth of data captured and ingested, ensuring completeness and relevance for business objectives.
Partner with data product owners to understand where current data exists, what can be re-purposed for use cases, identify data gaps, and document the nuances and challenges for specific data sets
Metrics & Baseline Assessment
Establish the current state of key performance and success metrics for existing and proposed use cases.
Analyze metric definitions, calculation methodologies, and data lineage to ensure accuracy and consistency.
Identify gaps in data capture, metric coverage, and reporting capabilities.
Stakeholder Engagement & Partnership
Conduct stakeholder interviews, build rapport, ask questions rooted in data domain experience (primarily marketing data, but other data sources like Nielsen, sales, panel, survey, etc.)
Partner with business, marketing, and technical stakeholders to elicit requirements and clarify objectives.
Translate findings into clear, actionable documentation, including requirements, user stories and acceptance criteria for technical teams.
Present insights and recommendations to both technical and non-technical audiences, including senior leadership.
Data Quality, Governance & Best Practices
Champion data quality standards, governance principles, and taxonomy management across all assessment activities.
Partner and collaborate with engineering lead and data modeling lead to ensure findings translate to development requirements and approach.
Business & Commercial Analytics
Analyze and interpret commercial performance data (e.g., volumetrics, ATL revenue, distribution, product mix) to identify trends and growth opportunities.
Translate commercial and consumer insights into actionable business recommendations.
Media & Marketing Analytics
Evaluate media performance across traditional and digital sources, including impressions, GRPs/TRPs, cost-to-client, and channel-level performance.
Work with digital media account data (Meta, TikTok, Snapchat, etc.) to derive and aggregate awareness, engagement, and conversion metrics.
Consumer & Brand Insights
Process and parse consumer survey data, ensuring sound handling of survey structures, questions, and response scales.
Build and interpret brand equity and consumption models, deriving meaningful KPIs for brand tracking and strategy.
Basic Qualifications:
7+ years of experience in data analytics, specifically marketing, consumer, and digital domains with extensive expertise in data discovery, profiling, and assessment methodologies. Experience should include:
Strong proficiency in identifying, integrating, and validating diverse data sources.
Expertise in baselining and benchmarking key metrics, identify gaps, and recommend improvements.
Excellent communication and stakeholder management skills.
Experience gathering and documenting requirements
Knowledge of data governance, taxonomy, and quality standards.
Familiarity with high-level data engineering concepts: data lake, data tables, metrics, dimension
Exposure to Agile and Product Management practices (backlog management, sprint planning, iterative delivery).
4+ years of experience in the Financial Services industry.
Preferred Qualifications:
Experience operating within cross-functional Agile teams.
Experience with marketing analytics, campaign measurement, and attribution models.
Ability to quantify and articulate incremental business value from data-driven initiatives.
LI-NA-FY25
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California $94,400 to $293,800
Cleveland $87,400 to $235,000
Colorado $94,400 to $253,800
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Illinois $87,400 to $253,800
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