Positions:2 Full Time
Experience
4 - 6 Years
Mandatory Skills
SAP Datasphere and SAP Analytics Cloud
Skill to Evaluate
SAP Datasphere and SAP Analytics Cloud
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 eg pandas NumPy scikit learn statsmodels
Experience using Python to pull and integrate data from diverse systems and APIs eg relational databases MySQL PostgreSQL REST APIs and third party sources eg 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 and commercial data is organized eg FI CO SD MM
Experience with data cleaning and building trustworthy analytics ready datasets
Domain
Working knowledge of Finance Accounting and Commercial concepts eg 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 eg 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
1
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 eg 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 eg 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
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