:

- Join a collaborative data engineering team where your work directly powers analytics reporting and data driven decisions
- In this role you ll help design and build reliable data pipelines on Databricks transforming raw data into trusted high quality datasets that teams can confidently use
- You ll partner closely with stakeholders to understand data needs improve performance and ensure smooth end to end delivery from ingestion to transformation and validation
- If you enjoy solving real world data challenges optimizing distributed processing and continuously improving how data platforms operate this opportunity will help you grow your technical depth while contributing to impactful outcomes
- You ll work in an environment that values ownership learning and practical innovation where clean engineering and teamwork go hand in hand

Key Responsibilities:

- Key Responsibilities
- Develop and maintain scalable ETL pipelines using Databricks and PySpark to process large datasets efficiently
- Implement data transformations cleansing and enrichment logic aligned to business and analytics requirements
- Optimize Spark jobs for performance and cost by tuning partitions caching and cluster configurations where applicable
- Build reusable notebooks jobs and support scheduling orchestration of workloads within the Databricks workplace
- Perform data validation reconciliation and quality checks to ensure accuracy and reliability of curated datasets
- Troubleshoot pipeline failures analyze logs and resolve issues to maintain stable production operations




- Collaborate with cross functional teams to gather requirements provide estimates and deliver enhancements iteratively
- Maintain clear technical documentation for pipelines transformations and operational runbooks

Technical Requirements:

- Primary skills Technology Data Engineering Databricks

Additional Responsibilities:

- Minimum Qualifications
- Bachelor s degree or equivalent in Engineering Technology Computer Science or related field BTech BE MSc or equivalent
- 3 5 years of experience in data engineering or related roles with hands on Databricks experience
- Strong hands on development experience with PySpark for distributed data processing
- Proven experience building and supporting ETL pipelines in production environments
- Ability to analyze data issues debug Spark ETL jobs and implement reliable fixes
- Preferred Qualifications
- Experience designing end to end data workflows on Databricks including job scheduling monitoring and operational support
- Solid understanding of data modeling concepts and building curated datasets for analytics use cases
- Familiarity with Delta Lake concepts ACID tables incremental processing upserts merges and best practices for lakehouse implementations
- Experience with performance tuning techniques for Spark workloads and handling large scale datasets efficiently
- Exposure to CI CD practices for data pipelines and maintaining code quality through reviews and standards
- Good to have skills
- Delta Lake Spark SQL Data Modeling Workflow Orchestration Performance Tuning

Preferred Skills:

Technology->Data Engineering->Databricks

📌 Databricks (Bengaluru)
🏢 Infosys
📍 Bengaluru