Senior Developer - SAP S4HANA, Java, SAP PLM at HCLTech · HyrikoBack to jobsvia Career pages·3w ago
Senior Developer - SAP S4HANA, Java, SAP PLM
HCLTech
Full-timeOn-site
Location:BangaloreType:Full-timePosted:3w ago 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.
Python for data wrangling, EDA, and modeling (e.g. pandas, NumPy, scikit-learn, statsmodels).
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
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Working knowledge ofFinance, 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.
- Demonstrated experience with
forecasting and/or anomaly detection on business data.
- Comfort with the full analytics lifecycle: EDA → RCA → insight → recommendation.
- 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.
S/4HANA
BW/4HANA
- 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.
Python for data wrangling, EDA, and modeling (e.g. pandas, NumPy, scikit-learn, statsmodels).
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.
Python for data wrangling, EDA, and modeling (e.g. pandas, NumPy, scikit-learn, statsmodels).
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
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.
- Demonstrated experience with
forecasting and/or anomaly detection on business data.
- Comfort with the full analytics lifecycle: EDA → RCA → insight → recommendation.
- 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
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