Senior AI-First Data Engineer

smartcatplatforminc; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote-friendly team

Global collaboration hubs

AI-native tooling

Job summary

smartcatplatforminc seeks a senior data platform leader to drive the transition to a cloud-native, streaming-first foundation that serves analytics, automation, and AI agents. You will design AI-ready data assets, semantic layers, and governance across the data stack.

You will mentor engineers, shape architecture decisions, and partner with Product, Engineering, GTM, and AI stakeholders in a remote-friendly, AI-native environment with distributed hubs worldwide.

Qualifications

• 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.

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.

Skills

Leadership

Communication

Tools

Databricks

dbt

Airflow

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