Data Engineer

• Pipeline Architecture: Design, implement, and manage complex data pipelines using Apache Airflow, with an emphasis on custom DAG development, dependency management, scheduling, and monitoring.
• Graph Database Management: Develop, optimize, and maintain Neo4J graph database solutions for high-volume, mission-critical enterprise applications.
• Data Handling: Work with both structured and unstructured datasets, leveraging appropriate storage and retrieval strategies.
• Collaboration: Collaborate closely with MLEs, software engineers, and product teams for pipeline delivery and data product enablement.
• Quality Assurance: Ensure data quality, reliability, and security throughout ETL/ELT pipelines.
• Operations: Monitor and troubleshoot workflows, ensuring high availability and performance of data systems.
• Documentation: Document processes, schemas, and best practices to ensure efficient knowledge sharing.

We are seeking a Data Engineer with hands-on experience in Airflow DAG development and Neo4J graph database management to architect, build, and optimize robust data pipelines within a dynamic engineering team. The ideal candidate exhibits mastery across modern data engineering tools and best practices, excels in designing scalable solutions, and thrives in collaborative, fast-paced environments., * Airflow: Proven experience architecting and developing Apache Airflow DAGs in production environments.

• Graph Databases: Demonstrated expertise in Neo4J and Cypher query language, including data modeling for graph structures.
• Programming: Advanced proficiency in Python and/or other modern programming languages used in data engineering.
• Database Fundamentals: Strong SQL skills and hands-on experience with distributed data stores (e.g., Postgres).
• ETL/ELT: Deep familiarity with data warehousing, ETL/ELT methodologies, and data pipeline orchestration.
• Cloud & Infra: Experience working with large datasets, cloud data platforms (Azure), and containerized environments (Docker/Kubernetes).
• Data Modeling: Solid understanding of data modeling, normalization/denormalization, and performance optimization.
• Engineering Standards: Commitment to writing clean, maintainable code with excellent problem-solving abilities.

Preferred Experience

• Experience Level: 3+ years of professional data engineering experience.
• Advanced Graph Tech: Prior work in Graph RAG and/or complex knowledge graphs.
• ML Ops: Experience supporting machine learning workflows or analytical data products.
• CI/CD: Exposure to CI/CD pipelines for data engineering and workflow automation.
• Domain Knowledge: Familiarity with MISMO data structures and mortgage industry data models is highly valued.