Fractional Analytics Architect – Modern Data Stack (Airbyte / PostgreSQL / Looker)
Description
We are seeking a senior Analytics Engineer / BI Architect to take ownership of our modern data stack and evolve it into a reliable executive decision-making system.
This is not a CRM implementation role. We are building and refining a data pipeline and business reporting suite that supports operational, financial, and strategic decisions.
Current Stack
Airbyte (ELT / data extraction)
PostgreSQL (data warehouse + transformations)
Looker (reporting and dashboards)
What We Need
We need someone who can:
Audit and stabilize our existing ELT pipeline
Refine and optimize our warehouse schema
Improve transformation logic and documentation
Ensure data accuracy and governance
Build and refine executive-level dashboards in Looker
Translate operational and financial workflows into structured data models
Identify areas where our architecture can be simplified or improved
This role requires both technical depth and business understanding. You should be comfortable discussing warehouse design, semantic layers, and KPI definitions in the same conversation.
The Type of Person We’re Looking For
8+ years in analytics engineering, BI architecture, or modern data stack roles
Direct experience maintaining end-to-end ELT pipelines
Strong SQL and PostgreSQL modeling skills
Experience with Looker semantic modeling (required)
Experience designing executive KPI dashboards
Comfortable working independently and advising leadership
Healthcare or high-ticket service business experience is a plus.
Engagement
Hourly engagement
15–25 hours per week to start
60–90 day initial scope with potential continuation
US-based preferred
Important
Please describe:
To Be Considered, Please Answer Clearly and Concisely:
Describe one end-to-end data system you personally owned.
What was broken or messy when you started?
What did you change?
What measurable outcome improved?
Describe a time when leadership used your reporting to make a business decision.
What decision was made?
What changed because of it?
Explain (in plain English) how you would model a multi-visit healthcare treatment plan that includes allocated inventory delivered over time.
What is the most common mistake companies make when building data infrastructure?
Applications that focus only on tools or technologies without business outcomes will not be reviewed.
Apply Now
Apply Now
We are seeking a senior Analytics Engineer / BI Architect to take ownership of our modern data stack and evolve it into a reliable executive decision-making system.
This is not a CRM implementation role. We are building and refining a data pipeline and business reporting suite that supports operational, financial, and strategic decisions.
Current Stack
Airbyte (ELT / data extraction)
PostgreSQL (data warehouse + transformations)
Looker (reporting and dashboards)
What We Need
We need someone who can:
Audit and stabilize our existing ELT pipeline
Refine and optimize our warehouse schema
Improve transformation logic and documentation
Ensure data accuracy and governance
Build and refine executive-level dashboards in Looker
Translate operational and financial workflows into structured data models
Identify areas where our architecture can be simplified or improved
This role requires both technical depth and business understanding. You should be comfortable discussing warehouse design, semantic layers, and KPI definitions in the same conversation.
The Type of Person We’re Looking For
8+ years in analytics engineering, BI architecture, or modern data stack roles
Direct experience maintaining end-to-end ELT pipelines
Strong SQL and PostgreSQL modeling skills
Experience with Looker semantic modeling (required)
Experience designing executive KPI dashboards
Comfortable working independently and advising leadership
Healthcare or high-ticket service business experience is a plus.
Engagement
Hourly engagement
15–25 hours per week to start
60–90 day initial scope with potential continuation
US-based preferred
Important
Please describe:
To Be Considered, Please Answer Clearly and Concisely:
Describe one end-to-end data system you personally owned.
What was broken or messy when you started?
What did you change?
What measurable outcome improved?
Describe a time when leadership used your reporting to make a business decision.
What decision was made?
What changed because of it?
Explain (in plain English) how you would model a multi-visit healthcare treatment plan that includes allocated inventory delivered over time.
What is the most common mistake companies make when building data infrastructure?
Applications that focus only on tools or technologies without business outcomes will not be reviewed.
Apply Now
Apply Now