Mandatory Skills : SAP Datasphere and SAP Analytics Cloud.

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.

Education Qualification : BE or Equivalent

Roles & Responsibilities :

- Must be willing to work in shift : 9 : 30 AM to 06 : 30 PM (all hours in IST), if there is any Emergency Support, he should be willing to extend and Provide Required Support.

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.