Senior Data Engineer

Key Responsibilities

• Develop and optimize ETL pipelines using Apache Airflow to ingest, transform, and load data from various sources into target systems.
• Implement production-ready code for data workflows, ensuring scalability, fault tolerance, and adherence to best practices such as modular design, error handling, and automated testing (unit, integration, and end-to-end).
• Collaborate with data scientists, analysts, and engineering teams to build and maintain RAG pipelines that enhance AI/ML applications with accurate, context-aware data retrieval.
• Participate in code reviews to enforce high coding standards, promote clean, readable code, and integrate CI/CD practices for automated testing and deployment.
• Monitor and troubleshoot data pipelines for performance, reliability, and data quality, implementing observability tools to detect and resolve issues proactively.
• Design and optimize data storage solutions, integrating with relational and NoSQL databases to support real-time and batch processing needs.
  

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 5+ years of hands-on experience as a Data Engineer or in a similar role, with a proven background as a strong developer (e.g., proficiency in Python, SQL, and related languages).
• Excellent proficiency with Apache Airflow for orchestrating complex ETL workflows, including DAG creation, scheduling, and dependency management.
• Demonstrated experience building scalable ETL pipelines that handle large datasets, with a focus on production-ready implementation including comprehensive test coverage (e.g., using pytest or similar frameworks).
• Strong emphasis on software engineering practices: Experience with peer code reviews, version control (e.g., Git), and ensuring code is modular, documented, and scalable to prevent common pitfalls like brittle or unmaintainable pipelines.
• Familiarity with data modeling, transformation, and integration in distributed environments.
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
  

Preferred Qualifications

• Experience with RAG pipelines, including vector databases and embedding techniques for AI-driven applications.
• Hands-on experience with databases such as PostgreSQL (for relational data), StarRocks (for analytical workloads), Cassandra or ScyllaDB (for high-throughput NoSQL), and Qdrant (for vector search).
• Knowledge of cloud data services (e.g., AWS Glue, Azure Data Factory) and orchestration tools beyond Airflow.
• Familiarity with monitoring and observability tools like Prometheus or OpenSearch for data pipeline health.
• Certifications in relevant technologies (e.g., Apache Airflow)