Data Engineer - ETL/Python (3-5 yrs)

Role : Data Engineer/Graph Developer

Location : Delhi

Experience : 3+ years

NOTE : Immediate joiners

Senior Data Engineer/Graph Developer :

We are seeking a Senior Data Engineer/Graph developer to lead the creation of a robust data ecosystem by creating RDF based ontologies and transforming raw data from various sources into an integrated data layer, utilizing Anzo graph Database/Azure/python/pyspark/sql based pipeline for for data preparation and processing, which will be loaded into Azure SQL/ADLS/Anzo data graph for business intelligence reporting and dashboarding. This role requires a hands-on approach to create RDF based ontologies, manage the entire data pipeline, from ingestion to visualization, ensuring high-quality and accurate data outputs to support business decision-making.

Key Requirements :

- 3-5+ years of experience in RDF ontologies, RDF based knowledge graph (Anzo graph DB preferred), Data modelling, Azure cloud and data engineering

- Strong expertise in python, pyspark, SQL, Azure (azure functions/ azure data factory / azure API services/ Azure DevOps )

- Understanding of ETL processes, Data pull using Azure services via polling mechanism and API/middleware development using Azure services.

- Strong ability to identify data anomalies, design data validation rules, and perform data cleanup to ensure high-quality data.

- Experience in pharma or life sciences data: Familiarity with pharmaceutical datasets, including product, patient, or healthcare provider data, is a plus.

- Experience in manufacturing data is a plus : preferably pulling data from OSI PI systems

- Project management and task planning experience, ensuring smooth execution of deliverables and timelines.

- Strong communication and interpersonal skills to collaborate with both technical and non-technical teams.

Example Responsibilities :

- Develop real-time graph data pipelines that capture and process relevant data, integrating vector databases for efficient similarity searches to uncover patterns indicative of fraudulent activities.

- Deploy the integrated solution to production, ensuring a robust and scalable infrastructure. Conduct benchmarking and performance optimization for both vector and graph databases to meet the demands of real-time fraud detection.

- Design and implement a cohesive system where vector and graph infrastructure complement each other seamlessly, combining their strengths to enhance fraud detection capabilities.

- Collaborate with cross-functional teams to identify specific use cases where vector and graph databases can work in tandem, devising data models that optimize the strengths of both technologies for effective fraud detection.

- Collaborate with the operations team to define and implement efficient maintenance and update strategies for both vector and graph databases, ensuring seamless operation in a dynamic production environment.