Graph Data Modeling & Design
Design and implement property graphs and RDF/OWL-based knowledge graphs.
Develop schemas/ontologies, entity resolution and lineage strategies; define best practices for graph modeling, naming, and versioning.
Data Engineering & Integration
Build and maintain ETL/ELT pipelines to ingest, cleanse, transform, and load data into graph stores from APIs, files, RDBMS, event streams.
Implement batch and streaming integrations using tools such as Airflow, dbt, Kafka/Kinesis, Spark/Flink.
Optimize data quality, deduplication, key management, and incremental upserts into graphs.
Write advanced queries in Cypher, Gremlin, and/or SPARQL; tune queries and indexes for performance.
Expose graph capabilities via APIs/services (REST/GraphQL/GRANDstack) with robust governance, observability and caching.
Performance, Reliability & Security
Capacity planning, clustering, backups, and high availability for graph databases.
Monitoring/alerting (e.g., Prometheus/Grafana, CloudWatch), profiling and query plan analysis.
Apply security best practices: encryption, RBAC/ABAC, least privilege, secrets management, and data masking/Pii handling.
MLOps/Analytics Enablement (nice if applicable)
Support downstream analytics and graph algorithms (PageRank, community detection, embeddings) and integrate with ML pipelines.
Infrastructure-as-Code (Terraform, Bicep, CloudFormation), containerization (Docker, Kubernetes), and CI/CD for data/infra.
Documentation, code reviews, and contribution to data governance (catalogs, lineage, metadata).
