Senior Data Engineer
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Key skills for this role
About the Role
Our Generative AI products are only as good as the data behind them. This role owns that data layer from end to end: the pipelines that bring data in, the transformations that s.
Key Skills for This Role
Responsibilities
- Build and run batch and streaming pipelines from source systems into the data lake and warehouse
- Build the data layer behind retrieval: source connectors, document parsing, chunking, embedding generation, and vector indexing
- Model curated, query ready datasets and metrics for AI and analytics consumers
- Add quality checks, validation, and monitoring to surface problems before they reach a model or user
- Apply row and column level access control, PII handling, and entitlement aware datasets
- Work with platform and DevOps engineers to expose data and retrieval as documented, dependable services
- Keep storage, compute, and query costs in check, especially for embedding and vector workloads
- Review code, write documentation, and help shape team standards
Requirements
- Experience building data pipelines for AI systems, not only for reporting
- Experience with chunking, embeddings, indexing, and keeping content current for LLMs or agents
- Proficiency in Python and SQL
- Experience with AWS data services
- Strong understanding of data modeling, schema design, and data quality
- Experience with access control, PII handling, and entitlement aware datasets
Full Job Posting
Role Overview
- Our Generative AI products are only as good as the data behind them. This role owns that data layer from end to end: the pipelines that bring data in, the transformations that shape it, and the way it reaches retrieval systems, agents, and analytics.
- The work runs on AWS, and the aim is a single governed source that every consumer can rely on.
- We want someone who has already built data pipelines for AI systems, not only for reporting.
- Preparing data for an LLM or an agent brings its own work around chunking, embeddings, indexing, and keeping content current, and you have done it before.
- The team is small and spans several languages, so you will own your pipelines and help set the standards the rest of us follow.
What You Will Do
- Build and run the batch and streaming pipelines that move data from source systems into the lake and through to the warehouse, owning the layers in between from raw to curated, along with their schema, quality, and lineage.
- Build the data layer behind retrieval: source connectors, document parsing, chunking, embedding generation, and vector indexing, including re embedding when content changes.
- Model curated, query ready datasets and metrics so AI and analytics consumers work from one definition instead of each rebuilding the logic.
- Add quality checks, validation, and monitoring so problems surface before they reach a model or a user.
- Apply access control where it belongs: row and column level rules, PII handling, and entitlement aware datasets, enforced as close to query time as the stack allows.
- Work with the platform and DevOps engineers to expose data and retrieval as documented, dependable services.
- Keep storage, compute, and query costs in check, with particular attention to the cost of embedding and vector workloads.
- Review code, write the documentation, and help shape how the team builds its data layer.
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