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.