ETL Consulting Services

• When this is the priority: KPIs don’t match across tools, reporting is slow, teams build “shadow models.”
• Typical outcomes: a reporting structure that teams actually use, clear refresh logic, performance tuned for peak windows (month-end, campaign peaks, board reporting).
• Deliverables: target model, KPI layer approach, executive/department marts, naming + refresh conventions.

The “perfect model” fails if nobody adopts it. We bias toward understandable tables, stable refresh schedules, and clear ownership—because that’s what prevents a second shadow layer from appearing three weeks later.

• When this is the priority: manual exports, brittle connectors, vendor APIs changing, and missing fields mid-quarter.
• Typical outcomes: predictable ingestion, fewer broken loads, faster onboarding of new sources.
• Deliverables: connector strategy (SaaS/DB/files), incremental ingestion patterns, handling for rate limits + schema drift, source health checks.

Integration breaks at the edges—rate limits, schema drift, partial loads, upstream backfills. We design for those failure modes from day one, because “happy path” ingestion is never the production reality.

• When this is the priority: KPI debates, duplicated entities, timezone/currency issues, and inconsistent joins.
• Typical outcomes: repeatable metrics, auditability, reduced rework, and “why did this change?” escalations.
• Deliverables: transformation rules documented in plain language, dedup/entity logic, standardization (currency/timezone), validation rules + anomaly detection.

If your team can’t explain a metric in one sentence, it’s not transformation—it’s guesswork. We document intent as clearly as the SQL, because most reporting failures are definition failures, not compute failures.

• When this is the priority: pipelines run, but nobody owns them; releases are risky; backfills are painful.
• Typical outcomes: steady-state operations, visible failures, safe deployments.
• Deliverables: production pipeline code patterns, version control + environment promotion (dev → stage → prod), Self-healing runs + retries, backfill strategy, runbooks + escalation paths.

“Hero-driven pipelines” always collapse under scale. Operational design (ownership + runbooks + change control) prevents recurring outages and prevents the hidden cost of 2 a.m. Slack firefights.

• When this is the priority, the worst incidents are “successful runs with wrong totals.”
• Typical outcomes: earlier detection of bad data, fewer executive escalations, faster incident resolution.
• Deliverables: source-to-target reconciliation checks, freshness/volume checks, business-rule validations, regression tests pre-deploy, alerting + run history dashboards.

Important clarification
Teams often mix these terms; experienced engineers won’t. We treat them as separate layers:

• Monitoring (ops): did the job run, how long did it take, did it fail, resource usage.
• Data quality testing (correctness signals): freshness/volume/schema/business rules—does the output make sense.
• Reconciliation (financial/metric tie-out): do totals match the trusted baseline or ledger, within agreed tolerances.
• Observability (visibility + history + impact): run context, lineage, change history, blast radius—what changed, who is affected, and since when.

The most expensive ETL bugs are not “job failed.” Their “job succeeded with wrong data.” That’s why we design tests and reconciliation to catch silent failures—not only infrastructure alerts.

• When this is the priority, forecasting/predictive use cases are planned, butthe training data isn’t stable.
• Typical outcomes (engineering-owned): consistent historical features tables, reproducible refresh schedules, reduced drift caused by changing data definitions.
• Deliverables (engineering-owned): feature-ready marts/tables, locked definitions + refresh logic, monitoring for data shifts in inputs.

Scope boundary (so responsibilities don’t get blurred): We can prepare stable, versioned, explainable datasets for ML—but feature definition strategy, label leakage prevention, and retraining cadence are often DS/ML ownership. We’ll coordinate, but we won’t pretend ETL consulting replaces ML governance.
Predictive work fails when training data changes underneath the model. We stabilize definitions and refresh behavior first—so ML teams aren’t fighting moving targets.