Data Engineering Lead

Location: United States or Canada Only >> Remote | Full-Time Industry: AI-Native Decision Intelligence | SaaS | Data Analytics

About the Role

We're looking for a Data Engineering Lead to own the full data pipeline powering SQOR.ai's decision intelligence platform, from source system ingestion through BigQuery, schema design, and the data layer that our ML models and AI agents reason against.

You'll work alongside our founding team and the 18 of us who have bootstrapped SQOR and are now rolling out across concurrent client engagements. This is a company that worked for years to develop its unique technology and vision for how companies will relate to their data and use analytics going forward. You are getting here just as it is starting to see its first clients, and you get to build the polish, the learning systems, and the feedback loops that further differentiate us from the pack of traditional business intelligence.

Our recent partnership with Google has opened enormous opportunity, as they are now actively selling SQOR to their clients.

🎥 Watch the reel Google made announcing our partnership: https://youtu.be/cu1YR2dRuUg?si=m0N_Gs59luwCkzN0

About SQOR.ai

SQOR.ai is an AI-native Decision Intelligence platform that represents a fundamental shift in how companies relate to their data. We don't build dashboards.

We don't wrap LLMs around SQL and call it AI.

Our platform separates machine learning from language generation by design. ML computes. The LLM explains. The result is a system where the math is deterministic, hallucination is structurally impossible, and every recommendation carries a confidence score informed by causal analysis, not correlation.

We connect directly to the systems companies already use, automatically extracting and analyzing thousands of KPIs to deliver instant answers, predictions, and recommendations. Our agentic framework shares a unified KPI ontology, a central causation engine, and a contextualization system that routes every query through domain expertise, company structure, and user-level personalization before generating a response.

Two provisional patents filed. Active client engagements generating revenue. Building deeply within the Google Cloud ecosystem.

What You'll Do

Drive the ingestion pipeline. Source system extraction (SQL Server, MongoDB, flat files, APIs), transformation, schema generation, and loading into BigQuery. You'll design multi-entity schemas across transactional tables, aggregation layers, dimension tables, and complex join logic. Speed matters here. Our ability to deploy in weeks instead of months depends on how fast clean data is available for the platform to work against... and lets just say we have interesting approaches.

Help drive NL2SQL accuracy. You'll own the schema mapping layer that connects natural language questions to correct SQL. Table names, column names, data types, join relationships, business term definitions, all wired into our contextualization engine so queries route deterministically. When enterprise data has overlapping tables or multiple paths to the same metric, you'll design the disambiguation logic that ensures consistent, correct answers.

Support the math layer. Derived metrics, cross-validation routines, variance decomposition, and scoring algorithm inputs. This is the substrate our agents reason against. You'll also build the data paths that support our closed-loop feedback architecture, where predictions, operator decisions, and actual outcomes flow back into the system to improve scoring over time.

Support the agentic framework. Our agents operate against a unified ontology and causation engine. The data layer must support agent-specific query routing, context-enriched SQL generation, and computation handoff between ML scoring and generative explanation. As new agents are built, the data infrastructure scales with them.

Deliver for clients. Hands-on work with real client data. You'll read ERP schemas, understand business logic (pricing, margin, cost allocation, customer hierarchies), and translate it into platform-ready structures. You'll be comfortable working directly with client CIOs, data engineers, and PE deal teams.

Mentor junior team members. Set the standard for code quality, documentation, and delivery predictability. Keep the team unblocked and moving. Create an environment where engineers ship good work on committed timelines.

Required Skills and Experience

• 8+ years in data engineering, at least 3 in a leadership role
• Deep hands-on BigQuery experience: schema design, query optimization, cost management, large-scale loading via GCS
• Strong MongoDB experience in production environments
• Production experience with NL2SQL or text-to-SQL systems against real enterprise data
• SQL Server extraction and migration experience
• Advanced Python proficiency
• Strong systems thinking across data engineering, ML infrastructure, and distributed architectures

Preferred Qualifications

• Experience with Snowflake, Databricks, SAP, Microsoft Fabric, and/or Amazon Redshift for migration and federation
• Mid-market ERP data environments: complex schemas, multi-entity structures, transactional and reporting table layers
• Working knowledge of ML scoring models, causal inference, or statistical decomposition
• Experience with agentic AI architectures: tool-calling, context injection, vector embeddings, retrieval-augmented generation
• PE portfolio company or management consulting background
• Production experience within Google Cloud; Vertex AI experience a plus

Why Join Us?

• Own the data engineering foundation of one of the most advanced Decision Intelligence platforms being built today
• Work on problems that don't exist in traditional BI: recursive extraction, causal math on live transactional data, deterministic query routing, closed-loop learning infrastructure
• Join a fast-moving, remote-first founding team where your work has direct, visible impact on every client engagement
• Equity-heavy compensation with real IP behind it: two provisional patents, active revenue, and a capital raise planned
• Be part of a paradigm shift, not an incremental improvement

Compensation

This role offers meaningful company equity based on experience and contribution. This is a founding-team-level opportunity. As we gain traction and raise capital, salaries will begin (expected in Q3). If you are optimizing for ownership in something with real engineering substance and strong prospects, we should talk.

How to Apply

• Send your resume and a war story to laz@sqor.ai. The hardest data engineering problem you solved in production. What broke, how you fixed it, and what you learned. No cover letters.