Etl Designv1.0.0

Migrating on-premise data warehouse to cloud-based Snowflake or Redshift. Use source contract to map schemas, incremental watermark for large tables, and validation checks to ensure data integrity during cutover.

Building a batch pipeline that updates customer profiles nightly from CRM, billing, and support systems. Extract via CDC, apply deterministic transforms for dedup, and load with upsert keys for idempotency.

ETL to load claims data from multiple providers into a central analytics platform. Use full snapshots for small claims tables, versioned transforms for business rules, and quarantine bad rows for audit.

Daily batch reconciliation of inventory between ERP and warehouse management system. Use incremental extract with updated_at watermark, row count validation, and backfill for missing days.

Building a data mart that aggregates clickstream, ad spend, and conversion data. Apply deferred transforms for late-arriving facts, partition by date, and validate with checksums.

Offer a managed ETL service that automates source contracting, incremental extracts, and idempotent loads. Revenue from subscription tiers based on data volume and pipeline complexity.

Provide consulting engagements to design and harden batch pipelines for enterprises, focusing on correctness, validation, and backfill strategies. Revenue from time-and-materials or fixed-price projects.

A SaaS product that validates ETL outputs with row counts, checksums, and referential checks, alerting on breaches. Revenue from per-pipeline or per-organization licensing.