SAP Data Engineer (Freelancer)

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.