This role is ideal for professionals with strong Databricks experience and a consulting mindset someone who can design, optimize, and scale data platforms that deliver real business value across varied industries and use cases.

Key Responsibilities :

- Architect and implement scalable data platforms using Databricks, Apache Spark, and modern cloud-native services.

- Lead end-to-end solutioning for complex data initiatives in multi-cloud environments (AWS, Azure, or GCP).

- Collaborate with clients and internal teams to understand requirements, define architecture, and guide development.

- Build and optimize robust CI/CD pipelines to ensure scalable, secure, and production-grade deployments.

- Design for performance and cost-efficiency, ensuring optimal use of compute and storage resources.

- Mentor and guide data engineers, sharing best practices in cloud data architecture, Spark optimization, and MLOps workflows.

- Stay current with Databricks innovations and integrate platform enhancements into active solutions.

Required Qualifications & Skills :

- 10+ years of experience in technology consulting, with 7+ years in data engineering, data platforms, and analytics.

- 6+ years of hands-on project experience using Databricks in enterprise environments.

- Databricks Certified Data Engineer Professional (or must be willing to complete certification within 2 months of joining).

- Deep expertise in at least one major cloud platform (AWS, Azure, or GCP) with working knowledge of at least one more.

- Strong hands-on skills in Apache Spark, including understanding of runtime internals and performance tuning.

- Experience in designing and maintaining CI/CD pipelines for data products.

- Familiarity with ML Ops practices and the deployment of ML models in production environments.

- Ability to assess and improve performance, scalability, and cost-efficiency across data workflows.

- Excellent communication and client-facing skills with a consulting mindset.