Data, analytics & AI

Data platforms and AI systems fail in the gap between experimentation and production: lineage breaks, costs spike, and models drift without owners. We implement vendor stacks as governed products—with engineering, risk, and finance aligned on the same metrics.

Warehouse workloads compete with ML training for budget and cluster capacity. We separate workloads with governance policies, autoscaling tuned to queue depth, and chargeback views that engineering managers recognize.

Storage tiering, compaction strategies, and incremental processing reduce scan costs without hiding data freshness risks from downstream consumers.

Event meshes connect CRM, ERP, and digital channels to analytics without brittle point-to-point integrations. Schema registries and compatibility rules prevent silent contract breaks when producers evolve.

Flink and Spark Streaming jobs include state recovery drills and backfill strategies for late-arriving data.

dbt projects with CI testing, environments, and promotion gates mirror application delivery discipline. Looker, Tableau, and Power BI semantic models are versioned with ownership tied to finance and business domains.

Metric definitions are centralized so executive dashboards stop arguing about denominators.

SageMaker, Vertex, and Azure ML are composed with feature stores, batch and online inference patterns, and GPU reservation strategies aligned to traffic curves.

Human review queues, bias testing where applicable, and model cards are integrated into release approvals—not paper exercises.

Enterprise agreements, data processing boundaries, and logging requirements are translated into network and key management designs engineers can implement.

Prompt injection defenses, retrieval grounding, and cost alerts protect both users and budgets.