Description:
Client: Truist Bank
Location: Atlanta, GA or Raleigh, NC (Hybrid-local candidates only)
Day-to-Day Job Duties: (What this person will do on a daily/weekly basis)
Design, develop, and optimize scalable data pipelines using Databricks, Python, PySpark, and SQL.
Build document ingestion, classification, extraction, validation, and transformation pipelines for PDFs, emails, attachments, Excel/XLSX files, insurance applications, Statements of Values (SOVs), and other business documents.
Integrate LLMs and AI models into data-processing workflows to extract structured information from unstructured and semi-structured documents.
Troubleshoot false positives, incorrect metadata, and inaccurate field values generated by LLM-based extraction pipelines.
Implement document classification, source-specific extraction rules, data validation, reconciliation, and quality-control mechanisms.
Transform extracted information into standardized JSON structures for downstream applications and underwriting workflows.
Design and implement ontology-based data models representing insurance entities, business concepts, and relationships.
Map extracted document attributes to standardized ontology concepts and metadata structures.
Optimize the processing of large and complex Excel/XLSX and SOV files, addressing performance, scalability, and concurrency challenges.
Implement reliable data persistence using PostgreSQL, Lakebase, or related database technologies.
Develop and maintain APIs and data products to support downstream application integration.
Collaborate with brokerage, underwriting, AI, and data engineering teams to align extraction outputs with business requirements.
Monitor production pipelines, troubleshoot data-processing issues, and continuously improve reliability, performance, and data quality.
Basic Qualifications: (What are the skills required for this job with minimum years of experience on each)
Minimum 7+ years of experience in data engineering, software development, or production data pipeline development.
Minimum 4+ years of hands-on experience with Databricks and scalable data-processing pipelines.
Minimum 5+ years of experience with Python, PySpark, and SQL.
Minimum 3+ years of experience processing unstructured and semi-structured data, including PDFs, emails, Excel files, JSON, and business documents.
Minimum 2+ years of experience integrating LLMs or AI models into document-processing or data-extraction pipelines.
Minimum 3+ years of experience implementing structured data extraction, document classification, validation, reconciliation, and data-quality controls.
Strong understanding of data modeling, schemas, metadata, entity relationships, and ontology or semantic modeling concepts.
Minimum 2+ years of experience designing JSON outputs, APIs, and data products for downstream application consumption.
Minimum 2+ years of experience with PostgreSQL, Lakebase, or similar relational database technologies.
Minimum 2+ years of experience troubleshooting production performance, scalability, concurrency, and data-processing issues.
Strong understanding of cloud-based data architectures, distributed processing, and pipeline optimization.
Strong analytical, problem-solving, communication, and cross-functional collaboration skills.
Degree:
Bachelor s degree in Computer Science, Information Technology, Data Engineering, Engineering, or a related field, or equivalent professional experience.
Nice to Have:
Experience in insurance, commercial insurance, brokerage, underwriting, or risk management.
Experience processing insurance applications, Statements of Values (SOVs), policy documents, and underwriting submissions.
Experience with LLM frameworks, prompt engineering, structured outputs, and AI evaluation techniques.
Experience building custom ontologies, knowledge graphs, or semantic data layers.
Experience with document AI, OCR, information extraction, and document intelligence platforms.
Experience with Delta Lake, Lakehouse architecture, Databricks Workflows, and Delta Live Tables.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Experience optimizing large-scale document ingestion and distributed processing workloads