Analytics Engineering Consultant for GTM Data

Overview: We are seeking an experienced Analytics Engineering Consultant to help design, build, and scale data feeds, transformation logic, classification frameworks, and business-facing dashboards. The goal is to move from manual or ad hoc analysis to repeatable, automated, and scalable analytics workflows supported by clean data pipelines, reliable data models, and well-defined business logic. This role is best suited for someone with strong experience in analytics engineering, data transformation, ETL/ELT, Snowflake, BI, and stakeholder-facing analytics delivery. Project Context: We are looking to operationalize analytics use cases that require data to be identified, cleaned, provisioned, transformed, synthesized, and presented in a reliable and scalable way. The consultant will help build the data foundation needed to support ongoing reporting, business intelligence, and decision-support use cases. This may include working with transactional, vendor, expense, product, customer, GTM, or operational data. A key part of the work will involve creating repeatable data workflows and classification/tagging logic that can evolve as additional data sources, business questions, or reporting needs are added. Key Responsibilities: -The Analytics Engineering Consultant will be responsible for: -Translating business requirements into scalable data and analytics requirements. -Identifying relevant data sources in Snowflake and/or other systems. -Extracting, cleaning, normalizing, and transforming source data into analytics-ready datasets. -Designing and building repeatable ETL/ELT workflows. -Creating reusable data models to support dashboards, reporting, and future analysis. -Developing tagging, classification, categorization, or enrichment logic for business-relevant data. -Supporting the evolution of business logic as new data sources or requirements are added. -Helping incorporate additional data sources over time, including enriched transaction-level data where applicable. -Building or supporting dashboards that present actionable business insights. -Documenting data definitions, transformation rules, assumptions, and limitations. -Partnering with Product, Analytics Engineering, Engineering, GTM, Finance, and business stakeholders to ensure data and insights can flow end-to-end. Required Skills: The ideal consultant should have hands-on experience with: -Analytics engineering or BI/data engineering. -Snowflake or similar cloud data warehouse platforms. -Advanced SQL. -ETL/ELT pipeline design and implementation. -Data modeling for analytics and reporting. -Data cleaning, transformation, and normalization. -BI/dashboard development using tools such as Power BI, Tableau, Looker, Sigma, or similar. -Rule-based classification, tagging, categorization, or enrichment logic. -Working with transactional, vendor, financial, spend, expense, customer, product, or operational data. -Translating ambiguous business requirements into reliable data assets. -Communicating clearly with both technical and non-technical stakeholders. Preferred Skills: Experience with any of the following would be a strong plus: -GTM analytics, finance analytics, product analytics, customer analytics, or operational analytics. -Expense management, corporate card, AP, invoice, procurement, or spend data. -Vendor intelligence, merchant normalization, or transaction categorization. -Python for data processing or automation. -Real-time or near-real-time data feeds. -Experience turning one-time analyses or prototypes into repeatable analytics products. -Experience working with Product, Engineering, Analytics Engineering, Finance, or GTM teams. What This Role Is Not: This is not primarily a data scientist or machine learning role. Data science experience is welcome, but the immediate need is for someone who can build the data infrastructure and analytics layer behind business reporting and decision-support use cases. The work focuses on data sourcing, preparation, transformation, classification, modeling, dashboarding, and operationalization. Expected Deliverables: Potential deliverables include: -Assessment of available source data and data gaps. -Recommended data model and architecture for scalable analytics feeds. -Automated or semi-automated pipeline from source systems to analytics-ready tables. -Defined tagging, classification, categorization, or enrichment logic. -Dashboard or reporting prototype. -Documentation of logic, definitions, assumptions, and maintenance requirements. -Recommendations for scaling the solution toward automated, real-time, or near-real-time reporting. Ideal Consultant Profile: The ideal consultant is a hands-on analytics engineer who can operate independently, clarify ambiguous business requirements, work across technical and business teams, and deliver clean, trusted, reusable data assets. They should be comfortable writing SQL, designing data models, working with imperfect transactional data, creating classification logic, and building dashboards that support business decision-making.