Map the data landscape — sources, targets, volumes, latency requirements, and system constraints. Define the high-level data flow architecture and identify integration patterns (batch, streaming, CDC) appropriate for each source-target pair.

Profile source schemas in detail — column types, nullability, cardinality, encoding, and semantic meaning. Identify type conflicts, naming inconsistencies, and data quality issues that will affect downstream transformation.

Review extraction implementations for reliability, idempotency, and operational safety. Verify that connectors handle schema drift, network failures, and partial extractions without data loss or duplication.

Implement extraction logic that reliably moves data from sources to the staging area. Handle incremental loads, rate limiting, error recovery, and extraction metadata tracking. Prioritize correctness and idempotency over speed.

Design and validate the target data model — grain definitions, entity relationships, surrogate key strategies, and slowly changing dimension (SCD) types. Ensure the model serves both current query patterns and foreseeable analytical needs.

Implement transformation logic that converts raw staged data into the target schema. Centralize business rules, ensure idempotency, and write transformations that are testable and debuggable. Substance over cleverness — readable SQL/code beats terse one-liners.

Review the validation suite for coverage completeness and assertion quality. Verify that tests cover all critical data paths, that thresholds are appropriately tight, and that failure modes produce actionable diagnostics rather than opaque errors.

Build and run data quality checks that verify schema compliance, referential integrity, uniqueness, accepted value ranges, row count reconciliation, and business rule correctness. Every assertion should be specific, automated, and produce a clear pass/fail/warning result.

Package and deploy the pipeline to the production orchestrator. Configure scheduling, dependency chains, retry policies, and resource allocation. Ensure the pipeline runs reliably on the target infrastructure with proper logging and observability.

Verify operational readiness — monitoring, alerting, runbooks, and incident response paths. Ensure the pipeline meets SLA commitments and that the team can diagnose and recover from failures without the original builder.