Overview

Role Overview
As a Solutions Engineer at Jedify, you will serve as the dedicated technical advisor for our most strategic customers. This is a high-impact, proactive role where technical consultancy meets data strategy.
You will be responsible for the long-term technical health of your accounts – moving beyond reactive troubleshooting to help customers optimize their semantic models, scale their AI agents, and refine their bespoke implementations. This role requires a blend of data analytics and modeling expertise, strategic thinking, and a “partnership-first” mindset to ensure our users extract maximum value from Jedify.

What You’ll Do
Strategic Technical Advising: Act as the primary technical point of contact for a portfolio of customers, deeply understanding their business goals and aligning Jedify’s Semantic Fusion™ and agentic capabilities to meet them.
Primary Technical Response: Serve as the first level of defense for all incoming issues reported by your customers. You will triage, investigate, and resolve technical hurdles, ensuring a seamless and rapid response experience.
Proactive Health Monitoring: Continuously analyze customer data models and performance metrics to identify potential bottlenecks or optimization opportunities before they become issues.
Solution Optimization: Partner with customers to refine their specific implementations, ensuring their usage is optimized for Jedify’s platform performance.
Engineering & Product Liaison: Act as a high-level advocate for your customers within Jedify. Translate complex customer needs into structured product feedback and work with Product to prioritize long-term technical enhancements.

Required Experience
7+ years of technical experience building, migrating, and optimizing Cloud Data Warehouses (Snowflake, BigQuery, Databricks, or Redshift), with hands-on expertise in performance-tuning complex SQL queries and managing NoSQL databases (e.g., MongoDB) alongside structured relational systems.
Advanced SQL & Python: Hands-on ability to write, debug, and refactor production-grade code for data pipelines and automated workflows.
Architecture & Modeling: Direct experience building scalable data models and semantic layers from scratch, spanning both relational and non-relational systems.
AI & Agentic Systems: Deep understanding of AI-driven applications, prompt engineering, and the architectural requirements for deploying agentic capabilities at scale.
BI & Platform Value: Proficiency in deploying and configuring BI platforms (Looker, Sigma, Tableau, or ThoughtSpot) to connect with complex data stacks and demonstrate platform value and ROI to customer stakeholders.
Technical Troubleshooting: Proven track record of reproducing bugs, performing root-cause analysis, and resolving deep technical blockers for enterprise customers.
Execution-Focused Communication: Ability to break down technical bottlenecks into clear, actionable steps for both customer business teams and internal product and engineering squads.
Communication: Exceptional ability to convey complex technical concepts to diverse audiences, from engineers to executive stakeholders, adapting tone and detail to drive clarity, alignment, and action.

 

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