[Remote] Data Engineer- AI/RAG Mandatory
Note: The job is a remote job and is open to candidates in USA. Dice is seeking a hands-on Data Engineer with strong backend engineering expertise and experience in financial services, analytics, or data-intensive environments. The role involves designing and optimizing data pipelines, modernizing ETL processes, and supporting AI/RAG-based solutions while ensuring operational excellence and effective communication with stakeholders.
Responsibilities
- Design, develop, and optimize scalable data pipelines, ETL workflows, and backend services
- Refactor and modernize existing data engineering processes to improve performance, maintainability, and reliability
- Perform extensive SQL optimization, data modeling, schema design, and PostgreSQL performance tuning
- Build and maintain APIs and data services using Python and FastAPI
- Develop and support cloud-native data solutions using AWS services and containerized environments
- Implement data validation, reconciliation, testing, and quality assurance processes
- Optimize Parquet-based storage, data processing, and query performance
- Support AI and Retrieval-Augmented Generation (RAG) initiatives through data preparation and retrieval frameworks
- Monitor data platform performance and proactively identify operational improvements
- Maintain detailed task trackers, timelines, risks, dependencies, and status reports
- Communicate project progress, blockers, and delivery updates to leadership and stakeholders
- Collaborate with engineering, analytics, architecture, and business teams to deliver high-quality solutions
Skills
- 5+ years of experience in Data Engineering, Backend Engineering, or related disciplines
- Strong Python development experience
- Hands-on experience with FastAPI and backend service development
- Deep expertise in PostgreSQL, SQL optimization, indexing, partitioning, and performance tuning
- Experience working with Parquet file formats and large-scale data processing
- Strong knowledge of ETL/ELT design patterns and data engineering best practices
- Experience with AWS services, preferably EKS, Lambda, S3, and cloud-native architectures
- Strong understanding of API design, data services, and backend systems
- Experience building scalable, production-grade data platforms
- Excellent analytical, troubleshooting, and problem-solving skills
- Strong communication skills with the ability to provide clear execution transparency and status reporting
- Experience working in Financial Services, Investment Management, Capital Markets, Banking, Analytics, or Data-driven business environments
- Ability to understand complex business data flows and translate requirements into scalable engineering solutions
- Must have exposure to AI-powered solutions, Retrieval-Augmented Generation (RAG), vector search, semantic retrieval, or LLM-based applications
- Experience with Kubernetes and container orchestration
- Experience with event-driven architectures and microservices
- Exposure to data observability, lineage, and governance frameworks
- Experience supporting analytics, reporting, or machine learning platforms
- Familiarity with Agile delivery environments
Company Overview
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