Job Title : Data Engineer

Experience : 2 4 years

Key Responsibilities :

- Architect and lead the development of scalable, reusable Python-based ETL pipelines to ingest, process, and manage data from complex enterprise systems such as SAP, OCPLM, and others.

- Drive data transformation strategies, ensuring raw data is cleaned, enriched, and optimized for downstream analytics, machine learning models, and visualization platforms.

- Collaborate cross-functionally with data scientists, analysts, and supply chain stakeholders to enable predictive and prescriptive analytics solutions that inform critical business decisions.

- Mentor and guide junior data engineers, promoting best practices in data pipeline development, automation, and code quality across the team.

- Lead initiatives in data automation, leveraging Python, cloud-native orchestration tools, and CI/CD practices to enhance efficiency and reliability.

- Oversee cloud data engineering efforts, particularly within Azure Data Lake and Databricks environments, ensuring solutions are secure, cost-effective, and scalable.

- Establish and enforce data governance standards, including data integrity, quality assurance, lineage tracking, and documentation throughout the pipeline lifecycle.

- Stay abreast of advancements in AI/ML, providing structured, production-ready data to accelerate model development and deployment in supply chain contexts.

- Influence architectural decisions, tool selection, and long-term data strategy in alignment with organizational goals.

Required Skills & Qualifications :

- 24 years of experience in data engineering or analytical programming roles.

- Strong programming skills in Python for data transformation and automation.

- Good command over SQL for querying and joining large datasets.

- Familiarity with Azure Data Lake, Databricks, or similar cloud platforms.

- Solid analytical thinking and problem-solving ability in working with complex, real-world datasets.

- Understanding of ML concepts like regression, classification, forecasting (hands-on not mandatory).

- Ability to understand enterprise data structures from systems like SAP or OCPLM, even without direct system access.

Tools & Technologies :

- Python, Pandas, NumPy, SQL

- Azure Data Lake, Databricks

- ML libraries : scikit-learn, XGBoost (as needed)