About the Role

We are looking for a Data Engineer with strong Generative AI exposure to design, build, and maintain scalable data pipelines and data platforms that power AI/ML and GenAI applications. The ideal candidate should have strong experience in modern data engineering tools along with hands-on Python development and API frameworks.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using modern data engineering tools.
• Develop and manage data transformation workflows using dbt and Dagster.
• Build robust data models and pipelines supporting analytics and AI workloads.
• Develop backend services and APIs using Python and FastAPI to enable data access for AI applications.
• Work with PostgreSQL and SQL to design efficient schemas and optimize queries.
• Process and analyze data using Pandas and Python-based data frameworks.
• Integrate structured and unstructured data pipelines to support Generative AI applications.
• Build AI agents using LangGraph
• Implement RAG pipelines and Text-to-SQL systems
• Integrate AI capabilities into enterprise platforms
• Collaborate with AI/ML engineers to enable LLM-powered applications and data pipelines.
• Implement data quality, monitoring, and performance optimization practices.
• Participate in architecture design discussions and contribute to scalable data platform design.

Required Skills

• Strong programming experience in Python.
• Experience working with Dagster for orchestration.
• Hands-on experience with dbt for data transformation and modeling.
• Advanced SQL skills and experience with PostgreSQL.
• Experience working with Pandas for data processing.
• Experience building APIs using FastAPI.
• Solid understanding of data pipeline design and ETL/ELT processes.
• Familiarity with building data infrastructure for AI/ML or GenAI use cases.

Good to Have

• Experience designing Star Schema / Dimensional Data Models.
• Familiarity with Medallion Architecture (Bronze, Silver, Gold layers).
• Experience with Generative AI frameworks or LLM integrations.
• Knowledge of RAG pipelines, vector databases, or embedding workflows.
• Exposure to cloud platforms such as AWS / GCP / Azure.

Vetting Process

• 1 hour technical discussion (Live coding Python)
• 30 minutes Technical Discussion
• 30 minutes Delivery Connect
• In person interview with Customer at NYC, NY