About the role
Description
AgencyBloc is a leading provider of agency management solutions built specifically for life and health insurance agencies. Our platform helps agencies streamline operations, strengthen client relationships, and drive growth through powerful CRM, commission processing, and marketing automation tools. We value innovation, collaboration, and making a meaningful impact in the industries we serve and we take pride in fostering a culture that genuinely cares about our customers, our work, and each other.
Requirements
Summary:
The Data, Revenue Analytics & Engineering is a hands-on technical role that builds and maintains the data AgencyBloc's go-to-market teams run on. Reporting to the Director of Revenue Operations, you will move revenue reporting off manual spreadsheets and onto AgencyBloc's Databricks lakehouse, and then reconcile what the new data shows against what the business has been reporting.
This role needs an engineer's habits and an analyst's judgment. You will write the Python and SQL that automate recurring reporting. You will trace numbers deal by deal until they reconcile. You will explain the results plainly to sales, client success, finance, and executive leaders. The goal is a single, governed source of revenue truth that answers most standard questions before anyone has to ask.
Responsibilities:
Data Platform Build & Migration
Rebuild core revenue reporting, starting with the consolidated GTM book, in AgencyBloc's Databricks lakehouse.
Build and maintain data pipelines from HubSpot, Sage Intacct, and other GTM systems into the lakehouse, through to a curated, report-ready (gold) layer.
Lift and shift department-built reporting tools, such as Client Success dashboards, onto the lakehouse so they run on governed data.
Replace manual, credential-dependent refreshes and hand-built spreadsheets with scheduled, automated jobs in Python and SQL.
Data Reconciliation & Quality
Reconcile lakehouse outputs against HubSpot and legacy reports deal by deal. Identify which records were left out and why, then correct the logic to bring them in.
Run new automated reports alongside existing manual processes until historical numbers align.
Build data quality checks for completeness, duplicates, and schema drift, and resolve issues at the source.
Reporting, Analytics & Forecasting
Build dashboards and data models that answer standard GTM questions on a self-service basis, reducing ad hoc report requests.
Support pipeline and revenue forecasting with clean data, and build statistical or predictive models (churn, pipeline, cohort analysis) as the platform matures.
Calculate and validate sales commissions and bonuses from system data, and document the rules behind each payout.
Metric Definitions & Data Governance
Document the calculation logic behind every metric you build, including bookings, expansion, new logo, renewals, and upsells.
Align metric definitions so Sales, Client Success, and FP&A start from the same base numbers.
Maintain the data dictionary and follow AgencyBloc's AI and Data Governance standards for access, lineage, and documentation.
Stakeholder Partnership & Communication
Gather requirements by asking stakeholders what decision they need to make, then choose how to build for it.
Take part in commissions and revenue reviews with sales and executive leadership, and explain the numbers and their sources clearly.
Present findings and recommendations to a range of audiences, from individual contributors to the executive team.
Show initiative: anticipate the next question, flag issues proactively, and carry work through to completion without step-by-step direction.
Skills/Education/Experience:
Required
Bachelor's or master's degree in Data Science, Statistics, Computer Science, Information Systems/Management, Business Analytics, Finance, or a related field, or equivalent experience.
3+ years in a data analyst, analytics engineering, or BI role, building production reporting rather than one-off analysis.
Strong SQL skills: complex joins, window functions, and query tuning against relational or lakehouse data.
Working proficiency in Python for data transformation and for automating recurring reports.
Demonstrated experience automating manual reporting processes, with measurable time saved or errors removed.
Experience reconciling data across systems and tracing discrepancies to the record level.
Hands-on dashboard and data modeling experience (Databricks AI/BI, Tableau, Power BI, or similar).
Clear written and verbal communication, including explaining numbers and their limits to senior leaders.
Self-directed working style with the judgment to ask the right questions and carry work through without step-by-step direction.
Preferred
Databricks experience (Delta Lake, Unity Catalog, Workflows/Jobs, notebooks).
HubSpot data model and reporting experience (deals, pipelines, companies, custom objects).
Experience with ERP or billing data, such as Sage Intacct invoice detail and ARR calculations.
Exposure to forecasting, statistical analysis, or predictive modeling (regression, cohort, churn) in Python, R, or SAS.
Experience with sales compensation or commissions calculations.
Familiarity with data governance practices: standardized metric definitions across business units, data dictionaries, lineage, and role-based access.
Version control (Git) and scripting (shell) for scheduled, auditable data jobs.
Background in B2B SaaS, insurance, financial services, or another regulated-data environment.
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Originally posted on Himalayas