Graph devops engineer
1 month ago
As a Knowledge Graph Engineer, you will:
Develop pipelines and code to support the ingress and egress of this data to and from the knowledge graphs.
Perform basic and advanced graph querying and data modeling on the knowledge graphs that lie at the heart of the organization's Product Creation ecosystem.
Maintain the (ETL) pipelines, code and Knowledge Graph to stay scalable, resilient and performant in line with customer’s requirements.
Work in an international and Agile Dev Ops environment.
This position offers opportunity to work in a globally distributed team where you will get a unique opportunity of personal development in a multi-cultural environment. You will also get a challenging environment to develop expertise in the technologies useful in the industry.
Primary responsibilities:
Translate requirements of business functions into “Graph-Thinking”.
Build and maintain graphs and related applications from data and information, using latest graph technologies to leverage high value use cases.
Support and manage graph databases.
Integrate graph data from various sources – internal and external.
Extract data from various sources, including databases, APIs, and flat files.
Load data into target systems, such as data warehouses and data lakes.
Develop code to move data (ETL) from the enterprise platform applications into the enterprise knowledge graphs.
Optimize ETL processes for performance and scalability.
Collaborate with data engineers, data scientists and other stakeholders to model the graph environment to best represent the data coming from the multiple enterprise systems.
Skills / Experience:
Must have:
Experience in designing and implementing graph data models that capture complex relationships, ensuring efficient querying and traversal.
Strong proficiency and hands-on experience in programming (e.g. Python, Java).
Practical work experience in the development of ontologies and methodologies (i.e. LPG/RDF), ideally in combination with complex data schemes and data modelling
Sound understanding and experience with use of graph databases, graph algorithms and data integration at large scale in complex business networks.
Experience with graph database query languages (e.g. SPARQL), graph algorithms, graph to SQL mapping, graph-based machine learning.
Experience in data integration, ETL processes, ETL tools and frameworks (e.g., Apache Spark, Apache Airflow) and linked data applications using tools like Databricks, Dydra.
Required proficiency in graph databases (e.g., Dydra, Amazon Neptune, Neo4j)
Nice to have:
Experience with AWS infrastructure (S3, CFTs, EC2), security and data.
Experience with AI pipeline technologies (RDS/Postgres, Snowflake, Airflow) and/or practical experience.
Familiarity with data warehousing and data modeling best practices
Understanding of data visualization tools (e.g., Tableau, Power BI)
Education & Personal skillsets:
A master’s or bachelor’s degree in the field of computer science, mathematics, electronics engineering or related discipline with at least 2 years of experience in a similar role
Excellent problem-solving and analytical skills
A growth mindset with a curiosity to learn and improve.
Team player with strong interpersonal, written, and verbal communication skills.
Business consulting and technical consulting skills.
An entrepreneurial spirit and the ability to foster a positive and energized culture.
You can demonstrate fluent communication skills in English (spoken and written).
Experience working in Agile (Scrum knowledge appreciated) with a Dev Ops mindset.
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