SAP Datasphere Consultant

Job Description
Technical
· Proven experience building data pipelines and models in SAP Datasphere (or SAP Data Warehouse Cloud / BW modeling).
· Hands-on dashboard development in SAP Analytics Cloud (SAC) — models, stories, and connections.
· Strong SQL for data extraction, transformation, and analysis.
· Proficiency in Python for data wrangling, EDA, and modeling (e.g. pandas, NumPy, scikit-learn, statsmodels).
· Experience using Python to pull and integrate data from diverse systems and APIs — e.g. relational databases (MySQL, PostgreSQL), REST APIs, and third-party sources (e.g. YouTube API) — into analytics workflows.
· Solid understanding of SAP data structures and storage nuances — key tables, master vs. transactional data, document flow, ledgers, and how SAP financial/commercial data is organized (e.g. FI/CO, SD, MM).
· Experience with data cleaning and building trustworthy, analytics-ready datasets.
Domain
· Working knowledge of Finance, Accounting, and Commercial concepts (e.g. P&L, balance sheet, cost centers, profit centers, GL, revenue, margin, pricing, AR/AP).
· Ability to connect data work to real financial and commercial outcomes.
Analytical & Modeling
· Demonstrated experience with forecasting and/or anomaly detection on business data.
· Comfort with the full analytics lifecycle: EDA → RCA → insight → recommendation.
Soft skills
· Strong communication skills; able to explain technical findings to Finance and business leaders.
· Self-starter who can own problems end to end with limited supervision.
Preferred / Nice-to-Have
· Experience with S/4HANA and/or BW/4HANA data models.
· Familiarity with SAP CDS views, HANA Calculation Views, or ABAP for data sourcing.
· Exposure to Git/version control, CI for analytics, or orchestration tools.
· Experience with cloud data platforms (e.g. BigQuery, Snowflake, Databricks) and integration into the SAP landscape.
· Knowledge of ML Ops or model deployment for production forecasting/anomaly workflows.
· Relevant degree in Finance, Accounting, Data Science, Computer Science, Statistics, Engineering, or equivalent experience.

Work arrangement
No