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.