Analytics Architect

If you geek out over semantic layers and governance frameworks as much as you do over whiteboarding solutions with clients, you might be exactly who we're looking for.

At InterWorks, Analytics Architects live at the intersection of technical depth and client partnership. You'll lead engagements from pre-sales through delivery, mentor consultants along the way, and help clients build analytics capabilities that actually stick after you're gone. It's a consulting role, which means variety is built in — and so is the opportunity to grow fast.

We're not looking for someone who just executes. We need someone who can architect solutions, lead delivery teams with confidence, and earn client trust quickly. The ability to bridge what's technically optimal and what a client can realistically adopt is what sets the best candidates apart.  Not deep in Sigma yet? If your dbt, BI and data modeling foundations are strong, we still want to hear from you. Sigma can be learned. The craft underneath it is harder to find.

Location Preferences:  This role has a strong preference for candidates in Raleigh-Durham, NC. Remote candidates in Central or Eastern time zones will be considered.  Remote work may be possible depending on the situation, let’s chat.

Salary Range:  $130,000–160,000, depending on experience and qualifications

What You’ll Do

• Lead enterprise analytics engagements end-to-end — from scoping and solution design through delivery, documentation, and client enablement.
• Architect and build governed metric definitions, reusable data models, and self-service analytics that scale.
• Design and deliver Sigma Apps, including input tables, write-back workflows, and workbook-as-product patterns.
• Design and implement AI-enabling data infrastructure: context pipelines, metadata layers, and governance frameworks that make data assets discoverable, trustworthy, and ready for AI consumption at scale.
• Architect systems integrations that connect CDWs, semantic layers, BI surfaces, and operational systems into coherent, governed data products.
• Serve as a trusted pre-sales partner — help scope engagements, contribute to proposals, and help prospects understand the art of the possible.
• Establish and evangelize software and analytics engineering standards — metric definitions, CI/CD, data model conventions, and governance patterns — across client organizations and internally at InterWorks.
• Mentor consultants on project-based engagements, providing technical guidance and supporting professional development.
• Build capability areas through internal solutions, reusable assets, and external partnerships.

What You’ll Need

Must-Haves:

• 5+ years in analytics or data engineering, with meaningful experience in an Analytics Engineering capacity - not just BI development.
• Strong, production-level SQL skills and hands-on experience with dbt - modeling, testing, documentation, and ideally semantic layer integration.
• Experience in Python for data transformation, pipeline logic, or analytics tooling - comfortable in code, not necessarily a software engineer.
• Experience with version control (Git) and modern software development workflows applied to analytics work (CI/CD, PR reviews, automated testing).
• Cloud data warehouse depth in at least one of: Snowflake, Databricks, or BigQuery.
• Experience with BI platforms like Tableau and PBI
• Proven success leading complex, multi-stakeholder projects with real delivery accountability.
• Strong communication skills, both technical and consultative.
• Comfort with ambiguity and a track record of bringing structure to evolving, client-facing situations.

Nice-to-Haves:

• Experience with Sigma, including Sigma AI Apps and input tables. If not Sigma, experience in modern BI tools that are tightly integrated with the CDW.
• Hands-on experience designing semantic or metrics layers (dbt Semantic Layer or similar), with a strong understanding of governed metric definition and reuse.
• Consulting or client-facing delivery experience - the ability to earn trust quickly, manage expectations, and leave clients more capable than you found them.
• Experience building applications natively within cloud data platforms such as Snowflake Native Apps, Streamlit in Snowflake, Cortex AI, or Databricks Lakehouse Apps and Model Serving. Comfort operating at the platform layer, not just on top of it.
• Exposure to AI/LLM integration patterns: experience engineering metadata pipelines, context layers, or retrieval infrastructure that connects AI systems to governed enterprise data.
• Background in data product design.
• Orchestration tool exposure (Airflow, Dagster, Prefect) and familiarity with ELT patterns and pipeline design.