Responsibilities

Lead the end-to-end architecture, design, and implementation of Databricks-based data platforms, ensuring solutions are scalable, secure, and aligned with enterprise standards and business goals.
Define best practices for data ingestion, transformation, modeling, orchestration, and performance tuning across batch and streaming workloads using Databricks, Spark, Delta Lake, and related ecosystem tools.
Collaborate with data engineers, analytics teams, cloud architects, and business stakeholders to translate requirements into robust technical solutions and delivery roadmaps.
Own technical governance for data quality, reliability, cost optimization, security, access control, and operational excellence across environments and projects.
Provide architectural guidance, code review leadership, mentoring, and hands-on support to engineering teams to ensure adherence to design principles and implementation standards.
Work closely with DevOps and platform teams to establish CI/CD, infrastructure automation, monitoring, logging, and incident response practices for Databricks workloads.
Evaluate new Databricks features, cloud services, and third-party integrations to continuously improve platform capabilities and business value.

Requirements

15 to 20 years of overall IT experience with strong expertise in data engineering, cloud data platforms, and solution architecture, including substantial hands-on experience in Databricks.
Deep knowledge of Apache Spark, Delta Lake, SQL, Python, and ETL/ELT patterns, with the ability to design highly performant and maintainable data pipelines.
Strong experience in cloud platforms such as Azure, AWS, or GCP, with preference for building enterprise-grade solutions on Databricks in production environments.
Proven ability to define architecture standards, lead technical discussions, influence stakeholders, and drive delivery across multiple teams and complex programs.
Solid understanding of data governance, security, IAM, networking, monitoring, and cost management for cloud-native data platforms.
Experience with workflow orchestration, CI/CD tools, version control, testing practices, and infrastructure as code concepts for data platform delivery.
Excellent communication, leadership, and mentoring skills, along with the ability to work in fast-paced environments and resolve technical challenges effectively