Senior Developer SAP S4HANA, Java, SAP PLM
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About this role
Job Summary
Mandatory Skills
SAP Datasphere and SAP Analytics Cloud
Skill to Evaluate
SAP Datasphere and SAP Analytics Cloud
Experience
4 to 6 Years
Location
Bengaluru
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.
Key Responsibilities
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.
Skill Requirements
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.
Other Requirements
Company
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