Data Engineer

Minimum 5+ years in Data Engineering, with at least 4 years of hands-on experience in Databricks and Azure ecosystem — this is a must.

• End-to-End Data Pipeline Delivery – Proven track record of building and owning ETL/ELT pipelines, data models, and platform components at enterprise scale across complex business domains.

• Expert SQL Skills – Advanced proficiency in complex joins, window functions, CTEs, query optimization, and performance tuning across large datasets.

• Strong PySpark & Python – Hands-on experience building distributed data processing pipelines, transformations, and automation scripts using PySpark and Python.

• Databricks Expertise – Deep working knowledge of Delta Lake, Unity Catalog, Workflows, medallion architecture (bronze-silver-gold), notebook development, and lakehouse best practices.

• Clean Code & Engineering Discipline – Writes modular, well-documented, and testable code. Follows coding standards, maintains version control hygiene, and builds reusable components that other developers can extend.

• Problem Solving & Debugging – Strong ability to diagnose and resolve complex data pipeline failures, distributed system issues, and production incidents under pressure.

• Performance Tuning – Demonstrated expertise in optimizing Spark jobs, SQL queries, and storage layers for cost efficiency, throughput, and reliability.

• Developer Ownership Mindset – Takes end-to-end responsibility from development through unit testing, deployment validation, and production support — not just writing code and handing off.

• Adaptability – Comfortable navigating evolving technology stacks, shifting priorities, and cross-functional collaboration in a dynamic, fast-paced Agile environment.

• Team Player – Collaborative engineer who actively contributes to code reviews, architecture discussions, knowledge sharing, and mentoring within a globally distributed team.

Nice to Have:

• AI Exposure – Familiarity with AI/ML concepts such as model training pipelines, feature engineering, RAG architectures, or tools like Azure OpenAI and LangChain.

• Experience working with supply chain data domains such as procurement, logistics, inventory, or demand planning is a strong plus. Understanding of SAP or ERP data structures in a supply chain context is an added advantage.

• Full Project Lifecycle & Cross-Team Collaboration – Has worked through the complete project lifecycle — from requirements gathering to production deployment. Experienced in collaborating with API and UI/UX teams, and understands how data flows end-to-end across upstream sources, backend services, APIs, and front-end applications.