Job Description

Databricks Data Engineer

Role Overview

We are looking for an experienced Databricks Data Engineer with 5–10 years of overall data engineering experience, including solid hands-on expertise in Azure Databricks, PySpark, SQL and Delta Lake.
The candidate capable of designing and developing scalable data pipelines and Lakehouse solutions. The role requires close collaboration with Data Architects, Business Analysts, Analytics teams and business stakeholders to transform enterprise data into reliable and business-ready data products.

Key Responsibilities
• Design, develop and maintain scalable data pipelines using Azure Databricks, PySpark, Python, SQL and Delta Lake.
• Implement Bronze, Silver and Gold Lakehouse data layers and reusable data-engineering frameworks.
• Develop complex ETL/ELT solutions involving SAP and other enterprise source systems.
• Ingest and transform data originating from SAP environments, applying appropriate business and transformation logic.
• Build data solutions supporting manufacturing business processes, reporting and analytics.




• Implement batch and incremental data-loading patterns, schema evolution, historical data handling and pipeline recovery.
• Develop and optimize complex Spark/PySpark workloads and SQL transformations for scalability and performance.
• Implement data-quality checks, validation, reconciliation, monitoring and exception handling.
• Develop analytical and dimensional data models, including Fact/Dimension and Star Schema models.
• Follow engineering best practices for Git, CI/CD, automated deployment, coding standards and documentation.
• Work with Data Architects and business stakeholders to translate technical and business requirements into scalable data solutions.
• Participate in design reviews, code reviews and continuous improvement of Databricks engineering practices.

Technical Skills & Certifications Essential Skills
• 5–10 years of experience in Data Engineering, Data Warehousing or Data

📌 Data Engineer (Pune)
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📍 Pune