Data Engineer

Responsibilities

Data Engineering & Pipeline Architecture:

• ScalablebDatabPipelines:Design,implement,andbmaintainhigh-through put batch and streaming ETL/ELT data pipelines using SQL, Python and modern data orchestration frameworks(e.g.,Airflow,Prefect,dbt).
• 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.

Knowledge Graph& Ontology Engineering:

• 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.

Machine Learning & AI Integration:

• GraphML & 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 Graph RAG(Retrieval-Augmented Generation), entity resolution, link prediction, and vector/graph hybrid search.
• Feature Store Integration: Maintain dataset versioning, lineage, and metadata catalos for both ML training data and graph nodes/relationships.

Required Qualifications

• Core Data Engineering: 3+yearsofdataengineeringexperiencewithexpertproficiency 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).

Preferred/Good-to-Have

• SkillsExperiencebuildingGraphRAGsolutionscombiningKnowledgeGraphswith Vector Databases (e.g., Pinecone, Weaviate, Qdrant) and LLMs.
• Experience with Graph Neural Networks(GNNs) or graph embedding algorithms (e.g.,PyG, DeepWALK).
• 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’sorMaster’sdegreeinComputerScience,DataEngineering,Software Engineering, or a related quantitative field.
• 3+yearsof 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.

Pay: ₹1,400,000.00 - ₹1,600,000.00 per year

Work Location: Hybrid remote in Chennai, Tamil Nadu