Narwhal's competitive advantage is our vessel-centric data layer, the structured knowledge graph that maps equipment, parts, suppliers, and workflows for every vessel in a fleet. This isn't analytics infrastructure or a data warehouse. It's the operational data foundation that makes AI agents work. You'll build the systems that ingest messy maritime data, structure it into usable form, and keep it synchronized across disconnected systems in real-time.
What You'll Do
- Design and build the vessel-centric data architecture: the ontology that structures equipment specifications, part numbers, supplier relationships, and workflow histories
- Develop data ingestion pipelines that extract, transform, and load data from ERPs, emails, PDFs, supplier catalogs, and vessel systems
- Build entity resolution systems that map the same part across different supplier numbering schemes, customer item codes, and manufacturer specifications
- Implement data quality frameworks: validation, deduplication, consistency checks, anomaly detection
- Create the data infrastructure that enables AI agents: feature stores, embedding databases, knowledge graphs
- Build real-time data synchronization between vessels, shore offices, suppliers, and forwarders
- Develop data monitoring and observability tools: track data freshness, completeness, accuracy
- Work with engineering to optimize database performance, query patterns, and data access
Who You Are
- 4+ years data engineering experience
- Strong skills in data modeling: schemas, relationships, normalization, denormalization
- Experience building ETL/ELT pipelines, data transformation workflows, and integration systems
- Solid understanding of databases: SQL, PostgreSQL, data warehouses, NoSQL when appropriate
- Comfortable with unstructured data: PDFs, emails, inconsistent formats, missing information
- Experience with knowledge graphs, entity resolution, or data mapping is highly valuable
- Strong Python skills for data processing, transformation, and automation
- Pragmatic about data quality: you understand the tradeoffs between perfection and usefulness
- Comfortable working directly with operators and technical teams during deployments, you'll debug issues on-site and explain technical decisions in operational terms
Nice to Have
- Experience in supply chain, logistics, or industrial operations data
- Background in document processing, OCR data cleanup, or unstructured data extraction
- Familiarity with vector databases, embeddings, or semantic search
- Understanding of maritime operations, part numbering systems, or procurement workflows
Why Join Narwhal
- Build data infrastructure that enables AI automation, not just analytics
- Work on genuinely hard data problems: messy, unstructured, inconsistent maritime data
- See your data systems powering production workflows immediately
- Direct impact: your data architecture determines what AI agents can do
- Equity stake in a company building the data backbone for global shipping
How to Apply
Send your resume and a note about why you're interested to careers@narwhal.ai
Tell us:
- What excites you about building for maritime operations
- A hard problem you've solved that's relevant to this role
- Why you want to join at this stage
We'll respond within 48 hours.
