Senior Data Engineer

NTT DATA Services • Charlotte, NC

1d

About The Position

NTT DATA is hiring a Senior Data Engineer to build and optimize data pipelines and document-processing workflows, including workflows that use Databricks, Python, PySpark, SQL, LLMs, and AI models. The role processes unstructured and semi-structured business documents, including PDFs, emails, attachments, Excel files, insurance applications, and Statements of Values (SOVs), and transforms extracted information into standardized JSON for downstream applications and underwriting workflows.

The engineer will design ontology-based data models for insurance entities and business concepts; map extracted attributes to standardized ontology concepts and metadata; implement data validation, reconciliation, and quality controls; and develop APIs and data products. The role also includes reliable data persistence, performance and concurrency optimization, production monitoring and troubleshooting, and collaboration with brokerage, underwriting, AI, and data engineering teams.

The position is based in Charlotte, North Carolina. Work arrangements may be remote or hybrid in some cases, but are subject to change based on client requirements; in-office attendance near an NTT DATA office or client site may be required for meetings or events.

Requirements

• At least 7 years of experience in data engineering, software development, or production data pipeline development.
• At least 4 years of hands-on experience with Databricks and scalable data-processing pipelines.
• At least 5 years of experience with Python, PySpark, and SQL.
• At least 3 years of experience processing unstructured and semi-structured data, including PDFs, emails, Excel files, JSON, and business documents.
• At least 2 years of experience integrating LLMs or AI models into document-processing or data-extraction pipelines.
• At least 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.
• At least 2 years of experience designing JSON outputs, APIs, and data products for downstream application consumption.
• At least 2 years of experience with PostgreSQL, Lakebase, or similar relational database technologies.
• At least 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.
• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, Engineering, or a related field, or equivalent professional experience.

Nice To Haves

• 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.

Responsibilities

• Design, develop, and optimize scalable data pipelines using Databricks, Python, PySpark, and SQL.
• Build ingestion, classification, extraction, validation, and transformation pipelines for business documents, including PDFs, emails, attachments, Excel/XLSX files, insurance applications, and SOVs.
• Integrate LLMs and AI models into workflows to extract structured information from unstructured and semi-structured documents.
• Troubleshoot false positives, incorrect metadata, and inaccurate field values produced by LLM-based extraction pipelines.
• Implement document classification, source-specific extraction rules, data validation, reconciliation, and data-quality controls.
• 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 processing of large and complex Excel/XLSX and SOV files, including performance, scalability, and concurrency.
• Implement reliable data persistence using PostgreSQL, Lakebase, or related database technologies.
• Develop and maintain APIs and data products for downstream application integration.
• Collaborate with brokerage, underwriting, AI, and data engineering teams to align extraction outputs with business requirements.
• Monitor production pipelines, troubleshoot processing issues, and improve reliability, performance, and data quality.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior