Design, build, and optimize scalable graph data pipelines and models using Python and SQL. Develop ingestion, ETL, validation, and query frameworks for graph databases, optimize performance and indexing, ensure data quality, collaborate with cross-functional teams, automate workflows, troubleshoot production issues, and document schemas and best practices.
This role is for one of the Weekday's clients
Salary range: Rs 1000000 - Rs 5000000 (ie INR 10-50 LPA)
Min Experience: 3+ years
Location: Mumbai,Bangalore
JobType: full-time
We are seeking a highly skilled Graph Data Engineer to design, build, and optimize graph-based data solutions that power complex relationships, connected datasets, and advanced analytics. You will play a key role in developing scalable data pipelines, graph models, and query frameworks that enable efficient exploration of interconnected data across large-scale systems.
The ideal candidate has strong expertise in Python and SQL, along with hands-on experience in data engineering, ETL development, and database optimization. Exposure to graph database technologies such as Amazon Neptune, TigerGraph, or Neo4j is highly desirable. This role offers the opportunity to work on cutting-edge data architectures and solve challenging problems involving highly connected data.
RequirementsKey Responsibilities
- Design, develop, and maintain scalable graph data pipelines and data integration workflows.
- Build and optimize graph data models to represent complex business relationships efficiently.
- Develop robust data ingestion, transformation, and validation processes using Python.
- Write optimized SQL queries for data extraction, transformation, reporting, and analytics.
- Design efficient graph traversals and queries to support business applications and analytical workloads.
- Collaborate with software engineers, data scientists, and product teams to understand data requirements and translate them into scalable graph solutions.
- Monitor data quality, consistency, and integrity across multiple data sources.
- Optimize database performance, indexing strategies, and query execution for high-volume datasets.
- Develop reusable data engineering frameworks and automation scripts.
- Troubleshoot production issues and continuously improve system performance and reliability.
- Document graph schemas, data pipelines, and engineering best practices.
- Strong proficiency in Python for data engineering, automation, scripting, and backend data processing.
- Excellent knowledge of SQL, including complex joins, query optimization, stored procedures, and performance tuning.
- Solid understanding of data modeling, ETL pipelines, and data integration techniques.
- Experience working with relational databases and large-scale datasets.
- Knowledge of distributed data processing concepts and scalable data architectures.
- Strong analytical and problem-solving abilities with attention to data quality and performance.
- Experience with graph databases such as Amazon Neptune, TigerGraph, or Neo4j.
- Knowledge of graph query languages such as Cypher, Gremlin, or GSQL.
- Familiarity with cloud platforms including AWS, Azure, or Google Cloud.
- Exposure to data orchestration tools such as Apache Airflow or similar workflow management platforms.
- Experience working with APIs, JSON, and semi-structured data.
- Understanding of containerization technologies such as Docker and Kubernetes.
- Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related field.
- 3–12 years of experience in data engineering, database development, or graph database implementations.
- Experience designing scalable, high-performance data systems for enterprise applications.
- Ability to work in Agile development environments and collaborate with cross-functional teams.
- Excellent communication and documentation skills.
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