SAP Data - 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.