AI Data & Knowledge Engineer – Senior Associate
State Street Corporation
We are looking for an AI Data & Knowledge Engineer with a strong foundation in data engineering and an interest in AI-powered data systems. This role will help build and maintain scalable backend data pipelines, ETL/ELT workflows, and structured knowledge assets that power enterprise AI applications. The ideal candidate will contribute to big data processing, data integration, retrieval pipelines, and knowledge graph foundations that enable reliable GenAI use cases.
Key Responsibilities
- Build and maintain backend data pipelines to ingest, transform, and serve structured and unstructured data for AI applications.
- Support ETL/ELT workflows across enterprise source systems, data platforms, and downstream AI services.
- Assist in developing RAG pipelines, vector indexing workflows, and knowledge graph assets under guidance from senior engineers.
- Contribute to data modeling, ontology creation, metadata tagging, and semantic enrichment of enterprise data.
- Support MCP server integration and AI data enablement tasks for domain use cases.
- Perform data quality checks, validation, reconciliation, and documentation for pipelines and data contracts.
- Help optimize pipeline performance, reliability, and scalability across batch and near-real-time workloads.
- Participate in code reviews, sprint ceremonies, and engineering discussions to build platform and domain knowledge.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
- 0–7 years of experience in data engineering, software engineering, or a related technical role.
- Working knowledge of Python and SQL and/or Java.
- Basic understanding of ETL/ELT pipelines, data transformation, and data integration concepts.
- Exposure to big data or distributed processing tools such as Spark, Databricks, or similar platforms is preferred.
- Basic familiarity with data lakes, warehouses, or lakehouse architectures.
- Understanding of data quality, metadata, and governance concepts.
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