Data Engineer

We are seeking a versatile Data Engineer with a hybrid background in traditional data engineering (SQL), semantic modeling (Ontologies), Graph Databases, and Machine Learning systems. In this role, you will design, build, and optimize the connected data architecture that powers our Enterprise Knowledge Graph (EKG), feature stores, and advanced ML pipelines.

You will bridge relational systems, graph databases, and ML models—building scalable data pipelines (ETL/ELT), transforming raw data into structured ontologies, and preparing graph-native features for downstream AI/ML applications like GraphRAG, link prediction, and recommendation systems.

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• Scalable Data Pipelines: Design, implement, and maintain high-throughput batch and streaming ETL/ELT data pipelines using SQL, Python, and modern data orchestration frameworks (e.g., Airflow, Prefect, dbt).
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• Relational & Analytical Data Stores: Write complex SQL queries, procedures, and transformations across data warehouses (e.g., Snowflake, BigQuery, PostgreSQL) to clean, aggregate, and stage raw business data.
• Data Integration: Extract structured, semi-structured, and unstructured data from transactional databases, APIs, and file systems to map into graph-ready schemas.
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• Graph Modeling: Build and optimize graph data schemas and property graph models (nodes, edges, labels) aligned with business domain ontologies.
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• Semantic Mapping & Ingestion: Translate semantic concepts (OWL, RDF, SKOS, SHACL) and relational SQL schemas into Graph Database models (Property Graphs or Triple Stores).
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• Graph Database Management: Administer, query, and tune performance on graph databases (e.g., Neo4j, AWS Neptune, Stardog, GraphDB) using languages like Cypher, Gremlin, or SPARQL.
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• Graph ML & Feature Engineering: Generate node/edge embeddings (e.g., Node2Vec, PyTorch Geometric) and engineer graph-based features for ML pipelines.
• ML Pipeline Support: Partner with Data Scientists and Machine Learning Engineers to operationalize ML models, enabling GraphRAG (Retrieval-Augmented Generation), entity resolution, link prediction, and vector/graph hybrid search.
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• Feature Store Integration: Maintain dataset versioning, lineage, and metadata catalogs for both ML training data and graph nodes/relationships.
  

• Core Data Engineering: 3+ years of data engineering experience with expert proficiency in Python and advanced SQL (window functions, query tuning, complex JOINs).
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• Graph Databases: 2+ years of hands-on experience building, querying, and managing graph databases (e.g., Neo4j, AWS Neptune, TigerGraph, Stardog) using query languages like Cypher, Gremlin, or SPARQL.
• Ontology & Semantic Web: Familiarity with data ontologies, taxonomies, and W3C standards (RDF, RDFS, OWL, SKOS, SHACL).
• Machine Learning Fundamentals: Practical experience deploying or supporting ML models, feature stores, and working with libraries such as PyTorch/TensorFlow, Scikit-learn, spaCy, or Hugging Face.
• Cloud & Orchestration: Proven experience with cloud platforms (AWS, GCP, or Azure) and pipeline orchestration tools (Apache Airflow, Prefect, or dbt).

• Experience building GraphRAG solutions combining Knowledge Graphs with Vector Databases (e.g., Pinecone, Weaviate, Qdrant) and LLMs.
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• Experience with Graph Neural Networks (GNNs) or graph embedding algorithms (e.g., PyG, DeepWALK).
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• Exposure to streaming architectures (Apache Kafka, Spark Streaming) for real-time graph updates.
• Knowledge of SHACL/ShEx for data quality validation in graph environments.
  

• Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related quantitative field.
• 3+ years of experience in software or data engineering, with at least 2 years actively working with semantic graphs or graph databases.
• Professional certifications from Google/Oracle/AWS are an added advantage for this role.