Job description
**Data Engineer **
What You’ll Do
- Data Engineering & Pipeline Development Design and maintain backend data ingestion and embedding pipelines. Work hands-on with JupyterHub to set up environments, clone repositories, and run complex workflows.
- Graph Database & Knowledge Management Use Neo4j to build graph-based solutions that map knowledge articles and workflows. Contribute to schema design, data integration, and performance tuning.
- Data Quality & Optimization Troubleshoot data quality issues, optimize Spark jobs, and manage partition sizes. Convert ECS direct connections to IO Meet using Spark and Python.
- Authentication & Security Solve JWT authentication challenges, research Kerberos for secure cluster connectivity, and manage credentials for Oracle DB and API clients.
- DevOps & Collaboration Work with GitLab for source control and Jira for project tracking. Participate in migrations from Azure DevOps to modern tools.
Your Tech Playground
- Programming & Tools: Python, JupyterHub, GitLab
- Databases: Oracle DB, PostgreSQL + PGVector
- Big Data: Spark, Iceberg, S3, Parquet, CSV
- Graph & AI: Neo4j, LangChain, LLM integration
- Security: HashiCorp Vault, Kerberos
- Visualization: Power BI for dashboards and reporting
Skills That Make You Stand Out
- Strong experience in data engineering, profiling, and ETL processes
- Expertise in Spark job tuning and large-scale data exports
- Familiarity with embedding models like BGEM 3 and Nomic
- Ability to manage high-volume workloads and optimize compute resources
- Knowledge of cloud and on-prem integration (Azure + on-prem systems)