SAP S/4HANA Finance → Snowflake

A global manufacturing client (DACH region). Client abstracted for confidentiality; metrics and scope as delivered.

Context

A large SAP Finance estate — GL, AR, AP, CO and AA ledgers across ~30+ company codes — needed a cloud-native analytics backbone. The challenge wasn't only volume; it was keeping the finance data trustworthy as it crossed from SAP into a lakehouse, so analytics could rely on it.

What I built

A cloud-native SAP-to-Snowflake pipeline on AWS:

• Ingestion and transformation with AWS EMR, Glue/PySpark and S3, provisioned via Terraform.
• A Snowflake lakehouse as the analytics catalog.
• Data contracts at the SAP↔lakehouse seam, so a change in an upstream ledger is caught rather than silently corrupting downstream analytics.
• An MVP scoped to the DACH region as the reference for wider rollout.

Impact

• ~30+ company codes in scope across the main finance ledgers.
• A multi-terabyte historical backfill plus 10–30 GB of daily delta ingest.
• A reference architecture the client could extend region by region.

Role & stack

Data engineer and technology architect (Accenture CTA group) — delivered the MVP and the reference architecture.

Stack: AWS (EMR, Glue, S3), PySpark, Snowflake, Terraform, Python.

→ See also Data & lakehouse.