SAP Data Engineer (Freelancer)

Mactores is the agent-native AWS modernization firm. We help large companies move their data, applications, and AI workloads to AWS, and we use agents to do it in weeks, where the rest of the industry still takes quarters or years.

Most modernization projects stall before they reach production. Ours don't. Our engineers ship real systems running in production, legacy retired, and outcomes you can point to. The way we work is built around that outcome from day one. Agents handle the repetitive work so engineers can focus on the judgment calls.

As our SAP Data Engineer, you'll own the SAP side of a modern data pipeline for enterprise customers running SAP on AWS. You'll design and ship the extraction layer that lifts data out of SAP HANA into S3 cleanly and incrementally, and you'll build the BW / BW4HANA load path that takes curated data back into the warehouse for business users to query.

You'll work alongside a PySpark engineer who owns the transformation layer on EMR, meeting them at the S3 boundary. You'll be the SAP voice on customer calls, and the person engineering counts on when raw SAP tables need to become datasets a business analyst can trust.

What you'll do

Build extraction pipelines from SAP HANA to AWS S3 using SLT, ODP, CDS views, SDI, and native HANA SQLScript, picking the right tool per source and per latency requirement.
Model raw SAP tables across FI/CO, MM, SD, and adjacent modules into clean, semantically meaningful datasets that the downstream Spark layer and business users can actually use.
Design and operate delta and CDC patterns so incremental loads stay correct, idempotent, and replayable.
Write ABAP extractors where standard SAP tooling falls short, and document them so future engineers can change them safely.
Own the write-back path: load curated data from S3 into SAP BW / BW4HANA and model it for end-user reporting and analytical querying.
Land data in S3 as Parquet with sane partitioning, schemas, and IAM scoping, and define the contract with the PySpark engineer at the ingestion-to-transformation boundary.
Embed with a customer team, ship the pipeline to production, and stay close enough through cutover to know it actually runs.

What we're looking for

Deep SAP HANA extraction experience: real production work with SLT, ODP / operational data provisioning, CDS views, SDI, and HANA SQLScript.
Strong grasp of SAP table structures and the business semantics behind them in at least one functional area (FI/CO, MM, SD, or similar), so you can turn raw tables into models a business analyst recognizes.
Solid delta and CDC instincts: you've designed incremental loads that survive reprocessing, late-arriving data, and source-side schema drift.
ABAP fluency sufficient to build custom extractors when standard tooling can't reach the data.
SAP BW / BW4HANA data loading and modeling experience for the consumption side of the pipeline.
Working AWS knowledge: S3 landing zones, Parquet, basic IAM, enough to collaborate confidently on an AWS-hosted pipeline without needing to own the platform.
Strong written and spoken English. You'll be on customer calls and working across geographies.

You'll be preferred if you

Have worked with AWS Glue Data Catalog or similar metadata layers over S3.
Have shipped SAP-to-cloud data lake patterns before, with opinions on what broke and how you'd do it differently.
Have done client-facing consulting or forward-deployed delivery, not just internal IT work.
Are comfortable collaborating with a Spark-based transformation layer at the S3 boundary, even if you don't write the Spark yourself.