Databricks Engineer

We are a technology consulting firm operating in Cloud Data Engineering and Analytics, helping enterprise customers build reliable, scalable data platforms and analytics products.
Our teams deliver end-to-end data lakes, real-time streaming pipelines, and production-grade ML feature stores using Databricks and modern cloud data tooling.Role & Responsibilities :

- Design, build, and maintain scalable batch and streaming ETL pipelines on Databricks using Delta Lake and Delta Live Tables (DLT).- Develop and optimize Spark/PySpark jobs for performance, cost-efficiency, and reliability; tune cluster sizing and autoscaling policies.- Implement data quality, observability, lineage and monitoring (alerts, dashboards, job health) to ensure production SLAs.- Collaborate with Data Engineers, Data Scientists and Architects to define schemas, partitioning strategies and data models that support analytics and ML use cases.- Build CI/CD and release automation for Databricks assets (notebooks, jobs, Delta tables); manage Git-based source control and code review processes.- Provide production support: troubleshoot job failures, perform root-cause analysis, and produce runbooks and post-incident improvement plans.Skills & Qualifications :

Must-Have :

- Databricks.- Delta Live Tables (DLT).- Apache Spark.- PySpark.- Delta Lake.- SQL.Preferred :

- Apache Airflow.- Azure Data Factory.- Git / CI-CD tooling (Azure DevOps, GitHub Actions, Jenkins).Additional Qualifications :

- Minimum 5+ years of professional data engineering experience with hands-on Databricks development; strong understanding of cloud data platforms (Azure/AWS/GCP) and best practices for data governance, security, and cost optimisation.
- Degree in Computer Science, Engineering, or equivalent practical experience is desirable.Benefits & Culture Highlights :

- Remote-first, India-based hiring with flexible working hours and a high-impact, fast-paced consulting environment.- Opportunities to work on multi-enterprise data platform projects, upskill on Databricks and cloud services, and attend technical training.- Collaborative teams, mentorship from senior engineers, and exposure to cloud architecture and ML productionisation.