AI Data Engineer

Data Engineer Overview

• 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.

Data Engineer Key Responsibilities & Duties

• 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.

Data Engineer Job Requirements

• 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.