The Data Infrastructure Your AI Initiative Actually Depends On

Building batch and real-time streaming pipelines that move data reliably from source to storage and processing. Covers schema validation, transformation logic, and error handling, so failures surface as alerts, not silent gaps someone finds later.

Designing a storage architecture that handles analytical queries and AI workloads on the same governed data. Covers table format selection and medallion architecture across raw, cleaned, and business-ready layers, with access patterns matched to the latency each workload needs.

Building the data layer AI systems depend on, distinct from the application itself. For RAG this means ingestion and embedding pipelines. For ML this means feature engineering, feature stores, and serving infrastructure at inference latency.

Data quality checks at ingestion, transformation, and serving so bad data is caught before it reaches a model, dashboard, or agent. Covers schema validation, outlier detection, and observability that turns freshness issues into actionable alerts.

Building the governance layer that makes data discoverable, trusted, and auditable. Covers metadata management, lineage tracking, access control, PII classification, and a catalog that lets teams find data without reverse-engineering the pipeline that made it.

Migrating legacy data warehouses, on-prem ETL pipelines, and siloed systems to modern cloud-native architectures built around current analytical and AI workloads, sequenced to keep existing reporting running throughout rather than forcing an offline cutover window.

Applying software engineering discipline to data pipelines: version-controlled definitions, automated testing, CI/CD for pipeline changes, and monitoring that catches failures before they hit downstream consumers. Untested pipelines degrade silently, and DataOps prevents that.