Capability
Data Engineering & Ops Analytics
Pipelines, warehouses, and reporting layers that turn operational data from many systems into numbers people can act on, with health checks that say whether the data is current, not just whether the job ran.
Problems I Solve
Where this capability fits
Operational data is fragmented across SaaS tools, APIs, and spreadsheets, and nobody can answer a clean pipeline question.
Executives act on stale or conflicting numbers because a job succeeded while the data went out of date.
Source APIs have rate limits and result ceilings that break naive pagination.
Dashboards saturate the database once more people start using them.
Scope
Capabilities
Layered warehouse design: raw ingestion, canonical models, operational metadata, reporting views
API ingestion with watermarks, resumable checkpoints, and bounded windows
Pipeline registries, run history, and freshness-based alerting
Identity resolution and revenue and payment attribution
Orchestration with Airflow, Prefect, or cron
Coverage reports and backfills that show gaps, not just row counts
BI enablement: Looker Studio, Power BI, Tableau
Connection pooling and query safety on shared databases
Example Systems
Systems I design and build
RevOps and operational analytics warehouses on GCP
API ingestion services for SaaS and public data sources
Pipeline observability and alerting layers
Dashboards built on curated, documented reporting views
Technical Stack
Tools and platforms
Need this capability on your product?
Start with a technical scoping call focused on data sources, users, and success criteria.