Senior Data Engineer

Growth Minded; United States; Remote; USD 45.000 - 60.000; Vollzeit

Zusammenfassung

Growth Minded is seeking a data engineer to own the data layer under InsightsOS and EngageOS, shaping architecture, pipelines, and analytical models for multi-location brands. You will report to the product lead and collaborate directly with the founder.

You'll work with SQL and Python, dbt, and warehouse tech like BigQuery and Postgres to develop semantic layers, governance, and scalable reporting across clients with 2 to 250 locations.

Qualifikationen

• 2+ years in data engineering with production analytics ownership.
• Strong SQL and Python, dbt or equivalent transformation framework, warehouse design.
• Experience with semantic or metrics layers and aggregation at scale.
• Practical ML: propensity scoring, forecasting, and anomaly detection.
• Comfortable making architecture calls without a spec handed to you and writing clear technical specs.

Aufgaben

• Design and own the semantic layer and aggregation architecture across clients with 2 to 250 locations each.
• Build and maintain pipelines from sources like Mindbody, Zenoti, Boulevard, Mariana Tek, Domo, QuickBooks Online, GA4, Google Ads, Meta, and Klaviyo.
• Develop trend, utilization, and propensity models that power proactive insights and lifecycle triggers.
• Work with Claude Code and AI-assisted tooling as a core part of your workflow, not an afterthought.
• Establish data quality, governance, and row-level security patterns as we move into multi-tenant, franchise-level reporting.
• Self-direct discovery and architecture decisions, and write clear technical specs the rest of the team can execute against.
• Join a daily product sync with the product lead and engineering team.

Kenntnisse

SQL

Python

dbt

Data warehouse design

Orchestration

Tools

BigQuery

Postgres

Jobbeschreibung

Headquarters: RemoteURL: https://growthminded.co

About the role

You'll own the data layer underneath InsightsOS and EngageOS, the products that turn booking, CRM, ad, and financial data from multi-location brands into proactive insights and automated actions. Today that layer is built and maintained by our product lead alongside two full-stack engineers. We need someone who owns it end to end: architecture, modeling, pipelines, and the analytical models on top. You'll report to our product lead and work directly with the founder.

Compensation: $45–60K plus revenue-share bonus (we profit share every month)

What you'll do

• Design and own the semantic layer and aggregation architecture across clients with 2 to 250 locations each.
• Build and maintain pipelines from sources like Mindbody, Zenoti, Boulevard, Mariana Tek, Domo, QuickBooks Online, GA4, Google Ads, Meta, and Klaviyo.
• Develop trend, utilization, and propensity models that power proactive insights and lifecycle triggers.
• Work with Claude Code and AI-assisted tooling as a core part of your workflow, not an afterthought.
• Establish data quality, governance, and row-level security patterns as we move into multi-tenant, franchise-level reporting.
• Self-direct discovery and architecture decisions, and write clear technical specs the rest of the team can execute against.
• Join a daily product sync with the product lead and engineering team.

Who you are

• 2+ years in data engineering with real ownership of a production analytics platform.
• Deep in SQL and Python, strong with dbt or an equivalent transformation framework, warehouse design (BigQuery, Postgres), and orchestration.
• Experience with semantic or metrics layers and aggregation at scale, you've solved the "same metric, different answers" problem before.
• Practical ML: propensity scoring, forecasting, and anomaly detection in a product context.
• Comfortable making architecture calls without a spec handed to you, and communicating clearly across time zones in writing.
• Bonus: multi-location, franchise, or subscription business data; marketing or CRM integrations; multi-tenant SaaS.

Why this role

• Full ownership of a product vertical, not a ticket queue.
• Small team, direct impact, your models show up in client revenue within weeks.
• Build the cross-client data asset that is central to our strategy.