AI Data Engineer

Green Key Resources; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

Green Key Resources is seeking a Data Engineer in New York to implement advanced data pipelines and AI systems enabling enterprise analytics. You will collaborate across domains to develop reusable data and AI patterns under architectural guidelines, operating within a federated data model for scalability.

The role emphasizes building AI-ready pipelines, semantic NL analytics, and contributing to architectural standards while mentoring engineers.

Qualifications

• 6+ years of technical experience with data engineering
• 1–2 years in AI/ML systems development preferred
• Expertise in Python, SQL, and distributed processing frameworks

Responsibilities

• Implement advanced data pipelines and AI systems to support enterprise analytics and automation
• Collaborate with domain teams to develop reusable data/AI patterns under architectural guidelines
• Operate within a federated data model to ensure scalability and consistency
• Build AI-ready data pipelines and retrieval systems for generative AI applications
• Contribute to semantic engineering for NL analytics and AI-driven insights
• Mentor junior engineers on modern data engineering practices
• Deliver foundational AI-ready pipelines and reusable patterns within the first year
• Participate in architectural reviews to evolve standards

Skills

Python

SQL

Spark

RAG pipelines

embedding workflows

Vector stores

AI/ML systems

Education

Bachelor's degree in computer science/data engineering

Master's degree preferred

Tools

dbt

Snowflake

Fivetran

Job description

• The Data Engineer role focuses on implementing advanced data pipelines and AI systems to support enterprise-wide analytics and automation initiatives.
• Collaborate with domain teams to develop reusable patterns for data and AI under architectural guidelines.
• Operate within a federated data model to ensure scalability and consistency across platforms.
• Build AI-ready data pipelines and retrieval systems to power generative AI applications.
• Contribute to semantic engineering efforts for natural-language analytics and AI-driven insights.
• Work closely with architects and engineers to refine enterprise standards and patterns.
• Deliver foundational AI-ready data pipelines and reusable engineering patterns within the first year.
• Operate in a collaborative environment to drive impactful data engineering solutions.
• Develop and maintain AI-ready data pipelines, embedding workflows, and indexing systems for enterprise data retrieval.
• Implement retrieval-augmented generation components and vector store integrations following architectural standards.
• Design and maintain ELT, streaming, and transformation pipelines using modern tools like dbt and Snowflake.
• Create semantic models and data products to enable trusted self-service analytics and AI grounding.
• Ensure data quality, lineage capture, and observability across AI and data workloads.
• Collaborate with governance teams to operationalize metadata and access controls.
• Mentor junior engineers on modern data engineering practices and contribute to knowledge sharing.
• Participate in architectural reviews and contribute hands-on expertise to evolving standards.
• Bachelor’s degree in computer science, data engineering, or related field; Master’s degree preferred.
• 6+ years of technical experience, including 1–2 years in AI/ML systems development.
• Expertise in Python, SQL, and distributed processing frameworks like Spark.
• Hands-on experience with modern data stacks, including dbt, Snowflake, and Fivetran.
• Proven ability to implement RAG pipelines, vector stores, and embedding workflows.
• Certifications in cloud, data engineering, or AI/ML are preferred.
• Strong engineering craft and attention to data quality and maintainability.
• Experience in financial services or regulated enterprise environments is a plus.