Strategy & Value Consultant - Data Analyst

Introduction

Job Responsibilities

Data Mapping and Modeling: 

• Map and document our data landscape — product usage, customer, CRM, and third-party sources — including schemas, definitions, grain, coverage, and known quality issues 
• Design and build analysis-ready tables and data models the wider team can query without re-deriving logic every time 
• Establish and maintain consistent metric definitions so the same question returns the same answer across dashboards, value models, and published research 
• Own data quality: validation, reconciliation against source systems, and monitoring for breaks 

Data Engineering and Operationalization:

• Build and maintain repeatable pipelines to extract, transform, and load data from a variety of sources, including APIs, flat files, and third-party datasets 
• Turn one-off analyses into maintained datasets with automated refreshes 
• Optimize queries and models for performance against large tables 
• Document transformation logic end to end so any published number can be traced back to source 

Analysis: 

• Perform complex analyses on large datasets: segmentation, cohort and time-series analysis, significance testing, correlation, and regression 
• Produce the quantitative foundation for industry benchmarks, maturity models, and industry-specific value models 
• Supply the datasets, analyses, and methodology notes behind our white papers and research reports, partnering with the team member who writes them 

Dashboards and Reporting: 

• Create and maintain dashboards for internal use, focused on customer benchmarks, performance metrics, and presales reporting 
• Build reporting stakeholders can self-serve, rather than re-requesting the same cut of data 
• Be the team's go-to resource for all data needs 

Collaboration and Communication: 

• Work with internal stakeholders to identify key data requirements and make sure data is accurate and consistent 
• Partner with cross-functional teams (Marketing, Product, Engineering, and Revenue Operations) to get access to the data we need and to understand how it's generated 
• Explain methodology, assumptions, and caveats clearly to non-technical stakeholders 
• Support live enterprise pursuits with the analysis behind tailored, insight-driven customer briefs 
   

What Makes This Role Unique:

• Embedded within a revenue-facing consulting function 
• Direct exposure to enterprise value discussions 
• End-to-end ownership of the data foundation, from raw source to published insight 
• Opportunity to build the analytical infrastructure for value selling from the ground up 

Knowledge and Experience

• 3+ years of hands-on experience as a Data Analyst, Analytics Engineer, or Data Engineer 
• Advanced SQL — window functions, CTEs, complex joins, and query optimization against large tables 
• Demonstrated experience modeling raw data into analysis-ready tables, not only querying tables someone else built 
• Experience integrating data from multiple disparate sources, including APIs and semi-structured formats such as JSON 
• Proficiency in Python (or R) for data manipulation and statistical analysis 
• Hands-on experience with a cloud data warehouse such as Snowflake, BigQuery, or Redshift 
• Strong working knowledge of Power BI (or Tableau or Looker) for dashboard development 
• Solid grounding in statistics: significance testing, confidence intervals, and the judgment to know  
• Clear written communication — enough to document methodology and explain caveats to non-technical colleagues 
• Strong analytical and problem-solving skills with a keen eye for detail 
• Solid organizational skills and the ability to manage multiple projects at once 

• Experience with transformation or orchestration tooling (dbt, Airflow, or similar) 
• Experience with go-to-market and product data models (Salesforce, product telemetry, billing data) 
• Familiarity with ROI or value-based modeling frameworks 
• Hands-on experience with AI tools applied to data work (for example, query generation, code assistance, or data profiling) 
• Exposure to SaaS, MarTech, or consulting environments 

Education

Competencies