Data Warehousing & Analytics Lake/warehouse architecture, conformed models, curated marts, and a semantic metric layer—fast, governed analytics. Stand up a modern data warehouse that turns raw feeds into trustworthy analytics. We design lake/warehouse architectures, model conformed dimensions and data marts, and operationalize governance, performance, and cost controls—so teams get fast, consistent insights across tools.
Key Benefits Single Source of Truth: Shared dimensions & certified KPIs
Speed & Scale: Star schemas, incremental materializations
Governed & Secure: RBAC/ABAC, PII masking
Cost Control: Partitioning, clustering, workload governance
Adoption & Trust: Metric/semantic layer and data catalog
What We Deliver Process Discovery & Targeting: map current-state, define bottlenecks and ROI, prioritize candidates. Workflow Design: intake forms, rules, approvals, queues, and human-in-the-loop steps. Automation & Integrations: triggers, webhooks, and connectors to existing systems. Exception Handling: retries, DLQs, escalations, and audit-ready logs. Dashboards & KPIs: SLA compliance, cycle time, backlog aging, and throughput. Runbooks & SLAs: ownership, cadences, and measurable commitments. Automation Patterns Approvals: multi-step routing with conditional rules and reminders. Requests & Intake: guided forms, validations, attachments, and status tracking. Data Sync: orchestration of create/update across systems with idempotency keys. Document Flows: OCR, classification, and extraction into downstream apps. Case/Incident Management: triage, assignment, SLA timers, and communication templates. Orchestration & Integration Event-Driven: webhooks/queues kick off flows; release markers for safe changes. System Handoffs: guardrails for sequencing, retries, and rollback. API-First: REST/GraphQL connectors with pagination, backoff, and rate-limit protection. Security, Access & Compliance Least-Privilege: role-based steps, approvals, and redaction for sensitive fields. PII Handling: masking/tokenization in non-prod; secrets management. Evidence: exportable logs, timestamps, approver identity, and change history. Observability & Reporting Metrics: cycle time, SLA hit rate, rework %, abandonment, queue depth. Traces & Logs: step-by-step spans per request for root-cause analysis. Dashboards: executive and ops views, with drilldowns by team and workflow. Delivery Approach Assess processes and define SLAs, roles, data, and success metrics. Design forms, rules, queues, and integration touchpoints. Build automation with tests, rollback paths, and change controls. Validate with UAT and pilot cohorts; measure before/after. Operate with release markers, runbooks, and continuous improvement. FAQs Q: Will automation break if an upstream system changes?
Q: Can humans approve or edit mid-flow?
Q: How do you secure sensitive data?
Reduce Cycle Time. Raise SLA Confidence. 
