The company is in the middle of a major business transition from PaaS to SaaS (ACCS and ACO). Leadership needs clear, fast, and trusted analytics to decide which customers to migrate, how to protect margin, and where to focus product and engineering effort. The Analytics Engineer is the person who makes that possible. This role owns the Commerce analytics engine — from building the right datasets to delivering the insights that drive migration, retention, and profitability decisions.
Responsibilities
Build the Analytics Foundation
Develop and maintain SQL-based datasets across Salesforce, Dynamics, Platform.sh, Snowplow, and cloud vendors
Create curated tables for customers, deals, margin, usage, and migration analysis
Partner with Data Engineering on pipelines (Snowflake, AWS Glue, PowerBI models)
Deliver Business-Critical Analytics
Own customer segmentation for migration readiness, margin, complexity, and churn risk
Build and maintain migration waves and prioritization models
Produce early warning signals for retention, support risk, and product adoption
Analyze time-to-launch, inactive customers, and product performance
Run pre- and post-impact analysis for ACCS and ACO launches
Turn Data Into Decisions
Translate business questions into structured, defensible analysis
Provide Finance, Product, and Leadership with a single, trusted view of customer value and risk
Explain not just what the data says — but what Commerce should do next
Requirements
What Success Looks Like
Leadership knows which customers to migrate, retain, or deprioritize
Migration plans are driven by data, not intuition
Churn and margin risk are visible before they become financial losses
ACCS and ACO performance is measured clearly and credibly
Commerce operates from one trusted segmentation and prioritization model
What You Bring
Strong SQL and analytics engineering skills
Experience building analytics datasets from CRM, product, and financial data
Ability to create segmentation, cohort, and prioritization models
Comfortable working with Finance, Product, Engineering, and Customer Success
A mindset focused on business outcomes, not just dashboards
Key Expertise
Advanced analytics engineering with a strong focus on building trusted, scalable datasets that power executive decision-making
Expert in SQL-driven data modeling, performance tuning, and designing curated fact and dimension tables for customer, usage, contract, and financial analytics
Hands-on experience developing Python and PySpark ETL workflows for large-scale data ingestion, transformation, and validation
Additional Information
Technology Capabilities
Data Warehousing & Databases: Amazon Redshift, SQL-based analytical data stores
ETL & Data Pipelines: AWS Glue, PySpark, Python-based batch and incremental pipelines
Business Intelligence & Analytics: Power BI (semantic models, DAX, executive dashboards)
Cloud Platforms: AWS, Azure, Google Cloud Platform
