Data Engineer (Data Science / Data extraction)
Project description
We're looking for a Data Engineer with hands on experience in graph databases to design, build, and optimize data pipelines and knowledge graph solutions that power advanced analytics and discovery. You'll collaborate with data scientists, platform engineers, and product teams to model complex domains, integrate heterogeneous sources, and deliver queryable, scalable graph data products.
Responsibilities
- Graph Data Modeling & Design o Design and implement property graphs and RDF/OWL-based knowledge graphs. o Develop schemas/ontologies, entity resolution and lineage strategies; define best practices for graph modeling, naming, and versioning.
- Data Engineering & Integration o Build and maintain ETL/ELT pipelines to ingest, cleanse, transform, and load data into graph stores from APIs, files, RDBMS, event streams. o Implement batch and streaming integrations using tools such as Airflow, dbt, Kafka/Kinesis, Spark/Flink. o Optimize data quality, deduplication, key management, and incremental upserts into graphs.
- Querying & APIs o Write advanced queries in Cypher, Gremlin, and/or SPARQL; tune queries and indexes for performance. o Expose graph capabilities via APIs/services (REST/GraphQL/GRANDstack) with robust governance, observability and caching.
- Performance, Reliability & Security o Capacity planning, clustering, backups, and high availability for graph databases. o Monitoring/alerting (e.g., Prometheus/Grafana, CloudWatch), profiling and query plan analysis. o Apply security best practices: encryption, RBAC/ABAC, least privilege, secrets management, and data masking/Pii handling.
- MLOps/Analytics Enablement (nice if applicable) o Support downstream analytics and graph algorithms (PageRank, community detection, embeddings) and integrate with ML pipelines.
- DevOps & SDLC o Infrastructure-as-Code (Terraform, Bicep, CloudFormation), containerization (Docker, Kubernetes), and CI/CD for data/infra. o Documentation, code reviews, and contribution to data governance (catalogs, lineage, metadata).
SKILLS
Must have
- Experience: 6 years in Data Engineering (or similar) with 2+ years focused on graph databases (property graph and/or RDF).
- Graph DBs: Hands-on with at least one of: o Property Graph: Neo4j, AWS Neptune (Gremlin/Cypher). o RDF Triple Stores: Ontotext GraphDB, Apache Jena/Fuseki, Blazegraph, Stardog, Neptune (RDF).
- Query Languages: Strong in Cypher and/or Gremlin; SPARQL if working with RDF/OWL.
- Data Pipelines: Proficient with Airflow (or similar), Kafka/Kinesis, Spark or Flink; building robust ETL/ELT at scale.
- Programming: Python (dataframes, APIs, CLI tooling); solid testing practices (pytest/pytest-bdd).
- Cloud: Experience with AWS managed graph/datastores, storage, compute, and networking basics.
- Performance & Ops: Indexing, memory/GC tuning, query plan analysis, partitioning/sharding concepts, HA/DR, backup/restore.
- Security & Governance: Secrets management, IAM, network isolation, PII compliance; familiarity with data catalog/lineage tools.
- Communication: Ability to translate domain knowledge into graph models and explain trade-offs to non technical stakeholders.
Nice to have
• Knowledge Graphs & Semantics: RDFS, SHACL, ontology engineering, reasoning/inference, vocabulary alignment (SKOS). • Graph Algorithms & Embeddings: Neo4j Graph Data Science, NetworkX, PyTorch Geometric, vector DB integration. • Graph + Search: Integration with Elasticsearch/OpenSearch, hybrid search (BM25 + embeddings). • Data Modeling: Experience migrating from relational to graph; CDC patterns (Debezium), event-driven architectures. • Observability: OpenTelemetry, tracing for data services; data quality frameworks (Great Expectations). • Delivery: Experience with productizing graph APIs, caching layers, SLA/SLO management. • Regulatory: Familiarity with GDPR/CCPA, data retention, sovereignty considerations.
