Senior AI-First Data Engineer

smartcatplatforminc; United States; Remote; USD 180,000 - 230,000; Full time

Benefits offered by this job

Remote-friendly distributed team

AI-native environment

Job summary

SmartCat Platform Inc. is seeking a Senior Data Platform Architect to lead the transformation from batch pipelines to cloud-native, streaming-first architectures that power analytics, automation, and intelligent agents.

You will design scalable data products and semantic layers for both human users and AI agents. You will mentor engineers, drive architecture decisions across teams, and collaborate with Product, Engineering, GTM, and AI stakeholders to raise the technical bar and ensure

Qualifications

• Senior-level experience designing and operating cloud-native data platforms at scale.
• Background building AI-ready data assets, semantic layers, and metadata-driven architectures.
• Familiarity with RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational data.

Responsibilities

• Lead evolution of cloud-native data platform, migrating pipelines from batch to near-real-time and streaming architectures.
• Build data products consumable by AI agents, analytics systems, and business users, including semantic layers and metadata management.
• Improve reliability, observability, governance, and performance across the data stack while establishing engineering standards.
• Use AI to accelerate development, testing, documentation, monitoring, and operational workflows.
• Enable AI agents to safely query, understand, and act on business data through well-designed interfaces and guardrails.
• Mentor engineers, conduct architecture reviews, and raise the technical bar across the data and analytics organization.

Skills

Databricks

dbt

Airflow

Python

SQL

AI-ready data assets

semantic layers

metadata-driven architectures

RAG systems

vector databases

knowledge graphs

Tools

Python

SQL

Job description

Role overview

Join a high-growth technology organization building an AI-native data platform that powers analytics, automation, and intelligent agents across the business. This senior role focuses on evolving the data architecture from legacy batch systems to a cloud-native, streaming-first foundation that serves both human and machine consumers. The position sits at the intersection of data engineering, AI enablement, and platform transformation.

Responsibilities

• Lead the evolution of a cloud-native, AI-first data platform, migrating pipelines from batch to near-real-time and streaming architectures
• Build data products consumable by AI agents, analytics systems, and business users, including semantic layers and metadata management
• Improve reliability, observability, governance, and performance across the data stack while establishing engineering standards
• Use AI to accelerate development, testing, documentation, monitoring, and operational workflows
• Enable AI agents to safely query, understand, and act on business data through well-designed interfaces and guardrails
• Mentor engineers, conduct architecture reviews, and raise the technical bar across the data and analytics organization

Requirements

• Senior-level experience designing and operating cloud-native data platforms at scale
• Strong hands-on skills with Databricks, dbt, Airflow, Python, and SQL
• Background building AI-ready data assets, semantic layers, and metadata-driven architectures
• Familiarity with RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational data
• Track record of mentoring engineers and leading architecture decisions across teams
• Ability to partner effectively with Product, Engineering, GTM, and AI stakeholders

Nice to have

• Experience transitioning from on-premise batch systems to streaming-first infrastructure
• Background enabling self-service analytics for non-technical stakeholders without sacrificing governance

Benefits and work setup

• Remote-friendly distributed team model with collaboration hubs in multiple global locations
• Environment described as AI-native, with active use of AI tooling across engineering and operations