Required Skills:

- SAP Datasphere
- SAP Analytics Cloud
- Python
- SQL
- SAP Data Structures
- Forecasting & Anomaly Detection
- Finance Domain Knowledge

Nice to Have:

- S/4HANA & BW/4HANA
- Cloud Data Platforms
- Git & CI/CD

Minimum Experience 4 Maximum Experience 6 Mandatory Skills SAP Datasphere and SAP Analytics Cloud Skill to Evaluate SAP Datasphere and SAP Analytics Cloud Experience 4 to 6 Years 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 · Solid 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. Education Qualificaiton BE Or Equalant Data Engineering & Pipelines • Design, build, and maintain data pipelines and models in SAP Datasphere (spaces, views, data flows, replication, and integration with source systems). • Ingest and harmonize data from SAP source systems (e.g. S/4HANA, ECC, BW/4HANA) and non-SAP sources into curated, analytics-ready layers. • Implement data cleansing, transformation, and validation logic to ensure accuracy, completeness, and consistency.



• Optimize models and queries for performance and cost, applying good practices for semantic layers and reusable views. Dashboards & Visualization • Build, publish, and maintain interactive dashboards and stories in SAP Analytics Cloud (SAC) for Finance, Accounting, and Commercial stakeholders. • Design clear, decision-oriented visualizations with well-defined KPIs, drill-downs, and self-service capabilities. • Manage data connections (live and import), models, and access within SAC. Analysis, Insight & Root-Cause • Perform Exploratory Data Analysis (EDA) to understand data quality, distributions, trends, and relationships. • Conduct Root-Cause Analysis (RCA) on financial and commercial variances, anomalies, and performance issues. • Translate analysis into actionable insights and recommendations communicated in plain business language to non-technical stakeholders. Modeling & Advanced Analytics • Build forecasting models on SAP data (e.g. revenue, cost, cash, demand, working capital) using appropriate statistical or ML techniques. • Develop anomaly detection to flag unusual transactions, postings, or patterns in SAP data for review by Finance / Controls. • Apply appropriate ML methods to prediction, segmentation, and pattern-detection problems, and validate model quality. Collaboration & Ownership • Partner with Finance, Accounting, and Commercial teams to gather requirements and prioritize deliverables. • Document pipelines, models, and dashboards; ensure reproducibility and maintainability. • Champion data quality and governance across the analytics stack.

📌 SAP Data - Senior SAP SAC Consultant (India)
🏢 Cigres Technologies
📍 India