Analytics Engineer

Good Inside•New York, NY

7d•$190,000 - $200,000•Hybrid

About The Position

Good Inside is seeking a Senior Analytics Engineer to own the foundation of our analytics data platform. This role blends analytics engineering and data engineering, requiring deep expertise in dbt-based modeling in SQL and hands-on experience building scalable, production-grade data pipelines leveraging Python. You will be responsible for designing modular, extensible data models that power Marketing, Product, Finance, and Growth decision-making—while ensuring correctness in complex domains such as subscription lifecycle management, Stripe payments, and revenue recognition. You will also be the connector between Engineering, especially with our infrastructure team for architecture decisions that have downstream data impacts, and the Data Analytics team. This role is ideal for someone who thinks like a software engineer, writes elegant SQL, embraces test-driven development, leverages AI-assisted workflows to increase velocity, and takes pride in building clean, reliable systems that scale. This is a hybrid role based in Manhattan with three days onsite.

Requirements

8+ years in analytics or data engineering roles
Deep hands-on dbt experience in production
Advanced SQL and strong Python for modeling and pipelines
Experience with Airflow and modern data warehouses (Snowflake)
Built and maintained ingestion pipelines (Fivetran or similar)
Strong understanding of subscription businesses, Stripe payments, and revenue modeling
Experience designing clean, modular data models for analytics use
Comfortable working with event-driven product data
Proficient with Git and collaborative engineering workflows
Embrace AI-assisted development tools to increase velocity
Make sound architectural decisions without heavy oversight
Balance speed with long-term maintainability
Clear communicator who thrives in fast-moving environments

Nice To Haves

Familiarity with tools like Segment, Braze, Amplitude, and BI platforms (Omni a plus)

Responsibilities

Build the Data Foundation
Design and maintain clean, reliable data models that power reporting and decision-making.
Create clear, well-documented transformations so everyone trusts the numbers.
Own Subscription & Revenue Data
Model the full subscription lifecycle; new signups, upgrades, churn, reactivations, and ensure our Stripe and billing data is accurate and aligned with Engineering and Finance.
Strengthen Our Data Stack
Build and maintain data pipelines and workflows.
Improve data freshness and reliability so teams aren’t waiting on or questioning the numbers.
Protect Data Quality
Set up strong testing and monitoring.
Catch inconsistencies early and keep definitions consistent across teams.
Partner Across the Business
Work closely with Engineering, Product, Marketing, Finance, and BI to make sure tracking is built in from the start and business questions translate into scalable data solutions.
Communicate clearly and balance competing priorities without lowering the bar.