SAP BW / Snowflake Data Engineer- Romania

We are looking for an experienced Data Engineer with strong expertise in SAP BW, data warehousing, SQL-based ELT, and modern cloud data platforms to support the migration of legacy SAP BW analytical layers to a modern cloud-based environment.
The role focuses on understanding and reverse-engineering existing SAP BW structures and business logic, then accurately translating them into dbt-based ELT pipelines on Snowflake. The successful candidate will work within an established Scrum team and will be responsible for ensuring that migrated data models and transformation logic maintain the same business meaning and results as the legacy system.
Responsibilities
Analyse and reverse-engineer existing SAP BW content, including InfoCubes, DSOs, InfoObjects, and BW transformations with ABAP routines.
Document existing data flows, dependencies, transformation rules, and business logic implemented within the legacy BW environment.
Migrate legacy DWH analytical layers to a modern cloud platform by rebuilding fact and dimension tables, transformation pipelines, and embedded business logic using SQL-based ELT.
Re-implement SAP BW transformation logic in dbt on Snowflake, ensuring semantic accuracy and preserving existing business rules without altering their intended meaning.
Develop and maintain scalable dbt models, using Jinja templating and macros in accordance with established project standards.
Design, implement, and maintain ELT orchestration workflows using Azure Data Factory, Apache Airflow, or equivalent technologies.
Perform comprehensive validation of migrated objects against the legacy SAP BW system to ensure data, transformation logic, and business rules remain equivalent.
Investigate and resolve data discrepancies between legacy and target environments.
Work as part of an established mixed internal/external Scrum team, contributing to migration throughput and the team's delivery cadence across 3-week sprints.
Collaborate closely with the Product Owner, internal engineers, and other stakeholders to clarify legacy logic, resolve ambiguities, and prioritise migration activities.
Use AI-powered tools to improve productivity, automate repetitive tasks, support technical decision-making, and enhance the quality of deliverables.
Apply AI tools responsibly by creating effective prompts, critically validating generated outputs, understanding their limitations, and taking ownership of the final implementation.
RequirementsMust-Have Skills
Proven hands-on experience in data transformation within a Data Warehousing environment, including the development or migration of:
Fact and dimension tables
Transformation pipelines
Business logic
Analytical data models
Strong SAP BW knowledge, with the ability to understand and reverse-engineer:
InfoCubes
DSOs
InfoObjects
BW Transformations
ABAP-based transformation routines
Advanced SQL skills and hands-on experience implementing complex transformation logic using SQL-based ELT.
Practical experience with dbt is strongly preferred.
Experience with ELT/data pipeline orchestration tools such as Azure Data Factory, Apache Airflow, or equivalent.
Strong understanding of Data Warehousing concepts and dimensional modelling, including:
Fact and dimension modelling
Slowly Changing Dimensions (SCD)
Partitioning strategies
Data transformation and integration patterns
Strong analytical and problem-solving skills, with the ability to translate legacy DWH logic into modern data platforms.
Comfortable working in a migration-focused environment, using existing SAP BW artefacts as the source of truth rather than designing a greenfield solution.
High attention to detail and strong focus on semantic accuracy, ensuring that existing business logic is preserved rather than reinterpreted.
Ability to work autonomously within an established delivery model and contribute effectively to a mixed Scrum team.
Regular use of AI tools to increase productivity, automate repetitive activities, support decision-making, and improve delivery quality.
Ability to critically evaluate AI-generated outputs and take full responsibility for the accuracy and quality of the final result.
Nice to Have
Snowflake experience, as Snowflake is the target platform. Candidates with strong SAP BW and DWH experience but limited Snowflake exposure will also be considered.
ABAP knowledge at a read/interpretation level, particularly the ability to understand existing BW transformation routines.
Python experience for pipeline scripting, automation, and data engineering tasks.
Familiarity with dbt Jinja templating and macro development.
Experience with AI-assisted development and code-generation tools, such as Snowflake Cortex.
Previous experience with large-scale SAP BW-to-cloud data warehouse migrations.
Experience working in Agile/Scrum environments.

Work arrangement
Yes