data engineer for SAP BW migration

The project involves migrating an existing SAP BW analytical landscape to a modern cloud data platform.

• Analyze and reverse-engineer SAP BW content, including InfoCubes, DSOs, InfoObjects, and BW transformations with ABAP routines, to document data flows and business logic;
• Migrate legacy DWH analytical layers to a modern cloud platform by rebuilding fact and dimension tables, transformation pipelines, and embedded business logic as SQL-based ELT;
• Re-implement BW transformation logic in dbt on Snowflake, preserving the semantic accuracy of legacy business rules;
• Develop and maintain dbt models using Jinja templating and macros in line with established project standards;
• Build and operate ELT orchestration workflows using Azure Data Factory, Apache Airflow, or equivalent tools;
• Validate migrated objects against the legacy system to confirm data and logic equivalence before cutover;
• Contribute to object-migration throughput and delivery cadence within an existing mixed internal/external Scrum team working in 3-week sprints;
• Collaborate with the Product Owner and internal engineers to clarify legacy logic, resolve ambiguities, and align migration priorities.

• Hands-on experience building and migrating analytical layers in a DWH context, including fact and dimension tables, transformation pipelines, and business logic translation;
• Strong SAP BW knowledge, including the ability to read and reverse-engineer InfoCubes, DSOs, InfoObjects, and BW transformations;
• Advanced SQL proficiency and hands-on experience implementing transformation logic as SQL-based ELT;
• Experience with ELT orchestration tools such as Azure Data Factory, Apache Airflow, or equivalent;
• Strong data warehousing fundamentals, including dimensional modeling, slowly changing dimensions (SCD), and partitioning strategies;
• Experience translating legacy DWH logic into modern platforms and working from existing BW artifacts as the source of truth;
• Strong attention to semantic accuracy and detail, preserving rather than reimagining business logic;
• Ability to work autonomously within an established Scrum delivery model;
• Regular use of AI tools to improve productivity, automate or reduce repetitive work, support decision-making, and deliver higher-quality outcomes;
• Ability to use AI tools responsibly by structuring effective prompts, critically validating outputs, understanding limitations, and taking ownership of the final result;
• Nice to have: Snowflake experience, ABAP read-level skills, Python for pipeline scripting and automation, familiarity with dbt Jinja templating and macro development, exposure to AI-assisted code generation tools such as Snowflake Cortex.

• Medical insurance;
• Remote access to an in-house fitness trainer and 1-on-1 remote sessions with a Sports and Health Coach covering nutrition counselling and tailored training plans;
• At least 21 vacation days, with one additional day for each completed year of collaboration, up to 29 days depending on tenure in the company and/or industry;
• Long-term learning programmes for technical and non-technical skills, with access to learning resources and platforms discussed with a Career Coach.