Senior Databricks Platform Engineer (Platform Administrator)

1. Solution Architecture & Design * Design end-to-end architectures using the Databricks Lakehouse Platform. * Architect scalable ETL/ELT pipelines, streaming solutions, and analytics platforms. * Define data models, storage strategies, and integration patterns. * Optimize cluster configurations, platform performance, and resource utilization.

2. Technical Leadership * Lead architecture discussions and technical design workshops. * Establish engineering best practices and reusable components. * Conduct code reviews and mentor data engineers.

3. Stakeholder Engagement * Translate business requirements into scalable technical solutions. * Prepare architecture diagrams, solution proposals, and implementation plans. * Collaborate with engineering, product, and analytics teams.

4. Data Governance, Security & Compliance * Implement Unity Catalog, data lineage, access controls, and workspace governance. * Establish monitoring, auditing, and data quality practices. * Align platform implementations with applicable security and compliance requirements.

5. Continuous Improvement * Evaluate new Databricks features and complementary technologies. * Identify opportunities for modernization, performance improvement, and cost optimization. * Improve data platform reliability, scalability, and operational efficiency.

We are seeking a Senior Databricks Platform Engineer to design, implement, and manage scalable, enterprise-grade data platforms using Databricks. The ideal candidate will have strong hands-on experience with data engineering, cloud platforms, platform administration, solution architecture, and stakeholder engagement., * Databricks: Clusters, notebooks, Delta Lake, MLflow, Unity Catalog, workspace administration, and governance.

• Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud Platform.
• Data Engineering: Apache Spark, Python, SQL, and distributed data processing.
• Solution Architecture: End-to-end data architecture, ingestion, transformation, storage, and analytics.
• Streaming Technologies: Structured Streaming, Kafka, AWS Kinesis, or Azure Event Hubs.
• DevOps: CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or similar tools.
• Platform Administration: Cluster configuration, performance tuning, access control, security, monitoring, and cost optimization.
• Leadership: Technical design discussions, code reviews, mentoring, and stakeholder collaboration.
• Methodologies: Agile development and delivery practices., * Databricks Data Engineer Professional or relevant architecture certification.
• AWS, Azure, or Google Cloud Platform cloud certifications.
• Experience with Tableau, Power BI, or Looker.
• Knowledge of machine learning workflows and MLOps.
• Experience with enterprise data modernization and cloud migration projects.