Migrate Databricks/Delta ETL pipelines to BigQuery with preserved incremental behavior, dedupe and late-arrival semantics—validated with idempotency simulations and proof-backed cutover gates.

Optimize Databricks→BigQuery workloads for predictable scan cost and fast SLAs: prune-first rewrites, partitioning/clustering, materializations, slot posture, and regression gates—so performance improves post-cutover.

Convert Databricks SQL (Spark SQL) to BigQuery Standard SQL with preserved semantics for MERGE/upserts, window logic, NULL/type coercion, and time handling—validated with golden-query parity and drift gates.

Migrate Databricks UDFs, notebook macro utilities, and procedural logic to BigQuery UDFs and stored procedures with preserved typing, control flow, and side effects—validated with replayable harnesses.

Prove Databricks→BigQuery parity with repeatable gates: MERGE correctness, KPI diffs, pruning baselines, idempotency and late-data simulations, and rollback-ready cutover criteria—so drift is caught pre-production.