SAP Data - Senior SAP SAC Consultant

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 Qualificaiton

BE or Equlant

SAP Data - Senior SAP SAC Consultant

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.