Customer Data Analyst

Fieldguide

6d•$145,000 - $175,000•Remote

About The Position

Fieldguide is seeking a Customer Data Operations Analyst to build and operationalize the data foundation connecting customer use of Fieldguide with measurement and communication of the value customers receive. The role sits at the intersection of Customer Success, Product, Data Science, and GTM Operations, translating customer needs into scalable data models, trusted reporting, and actionable insights. It combines SQL and analytics with hands-on data modeling and reporting to ensure customer data is accurate, accessible, and scalable. The analyst will participate in selected customer meetings, QBRs, and onsite engagements to understand reporting requirements. Most of the work is hands-on: developing and customizing reports, querying and validating data, building and maintaining data models, and addressing gaps in data infrastructure. The analyst will partner with Product and Data Science to ensure information is captured, structured, validated, and surfaced consistently. The role aims to establish a trusted customer data foundation for understanding adoption, engagement, efficiency, time savings, ROI, value realization, and business outcomes. Fieldguide is a remote-first company based in San Francisco, California. It describes its team as inclusive, driven, humble, and supportive, and emphasizes helping teammates grow.

Requirements

At least 2 years of experience in Data Analytics, Analytics Engineering, Data Operations, Business Intelligence, Customer Analytics, or a related technical data role.
Advanced SQL skills, including experience querying, transforming, validating, and troubleshooting complex datasets.
Strong understanding of dimensional data modeling, data transformations, metric definitions, and analytics architecture.
Strong understanding of data quality, validation, governance, and operational workflows.
Ability to translate business and customer questions into structured data requirements.
Strong analytical skills and comfort working with large or complex datasets.
Experience creating dashboards, reports, and executive-level data narratives.
Excellent communication skills, including the ability to translate between technical and non-technical stakeholders.

Nice To Haves

Experience at a B2B SaaS or enterprise software company.
Hands-on experience with dbt, including building, maintaining, testing, and documenting scalable data models.
Experience supporting Customer Success, QBRs, value realization, or customer analytics.
Familiarity with audit, advisory, accounting, or professional services workflows.
Experience working with product telemetry and customer usage data.
Experience partnering directly with Data Science or Analytics Engineering teams.

Responsibilities

Develop and customize customer-facing reports and dashboards, translating customer requirements into accurate, actionable reporting.
Use SQL and existing data infrastructure to investigate customer questions, validate metrics, and resolve discrepancies.
Build scalable reporting solutions reusable across customers, service lines, and engagement types.
Partner with Customer Success to support data-driven QBRs, executive business reviews, renewals, and value realization conversations.
Identify broader data-model or product-reporting gaps revealed by customer-specific requests.
Work with Product and Data Science to standardize and productize frequently requested reporting.
Develop and maintain scalable data models for customer reporting, QBRs, value realization, and product analytics.
Write advanced SQL to extract, transform, validate, and analyze customer and product data across multiple sources.
Build and maintain data models and transformations that produce reliable, reusable downstream datasets.
Identify and address legacy data quality, modeling, and structural issues affecting reporting accuracy.
Translate recurring one-off reporting requirements into standardized data models and reporting infrastructure.
Partner with Data Science and Data Engineering to determine whether reporting needs require analytics, data modeling, product instrumentation, or broader infrastructure changes.
Establish reusable datasets and semantic definitions for consistent cross-customer reporting.
Reduce manual reporting by identifying opportunities for automation and standardization.
Participate in selected customer meetings, QBRs, and onsite engagements to understand reporting requirements firsthand.