Senior Graph Data Engineer

We are looking for an experienced Senior Graph Data Engineer with strong expertise in enterprise graph data modeling, knowledge graphs, semantic technologies, graph databases, and large-scale data engineering.

The ideal candidate will have hands-on experience designing and implementing graph-based data solutions, developing ETL/ELT pipelines, integrating heterogeneous data sources, and building scalable distributed data platforms. Experience with graph query languages, Python development, cloud platforms, and modern lakehouse architectures is highly desirable.

Primary Skills Must Have

• Enterprise Graph Data Modeling: Property Graphs (LPG), RDF, ontologies, and semantic modeling.
• Graph Databases: Neo4j, Amazon Neptune, Dydra, or equivalent graph database technologies.
• Graph Query Languages: Cypher, SPARQL, and Gremlin.
• Python Development: Strong programming and data processing skills.
• ETL/ELT Development: Design, development, and optimization of scalable data pipelines.
• Big Data Technologies: Apache Spark, Databricks, Airflow, or similar platforms.
• Data Integration & Engineering: Integration of data from multiple enterprise sources.
• Data Modeling: Relational, analytical, graph, and lakehouse data modeling.
• Distributed Data Platforms: Experience designing and working with scalable data processing systems.
• DevOps & Automation: CI/CD pipelines, Infrastructure as Code (IaC), and deployment automation.
• Enterprise Data Architecture: Designing robust, scalable, and maintainable enterprise data solutions.

Secondary Skills Good to Have

• Databricks Lakehouse Platform
• Delta Lake and Unity Catalog
• AWS services: S3, Neptune, Glue, Athena, IAM, and Lambda
• Graph Analytics and Graph Data Science
• Graph-based Machine Learning
• Semantic Search and Knowledge Graphs
• Retrieval-Augmented Generation (RAG) solutions
• Recommendation Systems
• Data visualization tools such as Dash, Shiny, Grafana, Splunk, and Databricks Visualization

Key Responsibilities

• Design and implement enterprise-scale graph data models using LPG, RDF, ontologies, and semantic modeling techniques.
• Develop and optimize graph database solutions using Neo4j, Amazon Neptune, Dydra, or equivalent platforms.
• Build and optimize graph queries using Cypher, SPARQL, and Gremlin.
• Develop scalable ETL/ELT pipelines using Python, Apache Spark, Databricks, Airflow, or similar technologies.
• Integrate structured and semi-structured data from multiple enterprise systems.
• Design and implement graph-based data architectures that support analytics, semantic search, and knowledge discovery.
• Collaborate with enterprise architects, data scientists, and engineering teams to deliver scalable data solutions.
• Implement data quality, governance, security, and performance optimization practices.
• Establish CI/CD processes and Infrastructure-as-Code practices for reliable deployments.
• Evaluate emerging graph technologies and identify opportunities to improve data connectivity, analytics, and business outcomes.

Candidate Profile

• 10+ years of overall experience in data engineering, data architecture, or related technical roles.
• Strong hands-on expertise in graph data modeling, graph databases, and graph query languages.
• Proven experience developing production-grade Python applications and ETL/ELT pipelines.
• Experience with distributed data processing and modern data platforms.
• Strong problem-solving, analytical, and communication skills.
• Ability to work with enterprise stakeholders and deliver solutions in a client-facing environment.
• Willingness to work in a hybrid model from the client location in Hyderabad, Bengaluru, or Noida.