Databricks 专家

Key Responsibilities:
Architecture & Platform Design

• Design enterprise Databricks Lakehouse architectures aligned with the Databricks Well-Architected Framework

• Define reference architectures for batch, streaming, analytics, and ML workloads

• Select and standardize cluster, compute, and workspace architectures

• Design multi-workspace strategies (dev/test/prod, shared vs. isolated)

• Ensure architectures meet scalability, availability, and performance requirements

Well-Architected Framework Alignment
Apply Databricks best practices across all pillars, including:

• Security & Governance (Unity Catalog, IAM, data access controls)

• Reliability & Resilience (job retries, checkpointing, failure isolation)

• Performance Efficiency (cluster sizing, autoscaling, caching)

• Cost Optimization (compute policies, workload separation, monitoring)

• Operational Excellence (monitoring, automation, CI/CD, runbooks)

Implementation & Engineering

• Lead Databricks workspace, cluster, and Unity Catalog implementations

• Implement Delta Lake, Delta Live Tables (DLT), and Structured Streaming

• Build and optimize ETL/ELT pipelines using Spark and SQL

• Integrate Databricks with cloud services (S3/ADLS/GCS, IAM, Key Vault, networking)

• Establish CI/CD pipelines for notebooks, jobs, and infrastructure

Security, Governance & Compliance

• Implement Unity Catalog for centralized governance

• Define data classification, lineage, and audit strategies

• Enforce least-privilege access and secure networking patterns

• Support compliance requirements (HIPAA, SOC 2, PCI, GDPR as applicable)

Operations & Optimization

• Monitor platform health, performance, and cost

• Troubleshoot production issues across jobs, clusters, and data pipelines

• Perform workload tuning and cost-performance optimization

• Define SLOs, alerts, and operational metrics

Collaboration & Leadership

• Partner with Data Engineering, Analytics, ML, Platform, and Security teams

• Translate business requirements into technical architectures

• Provide architectural guidance and technical mentorship

• Communicate risks, tradeoffs, and recommendations to leadership

Required Qualifications:
Experience

• 7+ years in data engineering, analytics, or platform architecture

• 3–5+ years hands-on Databricks experience in production environments

• Proven experience applying the Databricks Well-Architected Framework

• Experience designing cloud-native lakehouse architectures

• Experience supporting mission-critical data platforms

Technical Skills

• Databricks Lakehouse Platform

• Apache Spark (PySpark / Scala / Spark SQL)

• Delta Lake, Delta Live Tables, Structured Streaming

• Unity Catalog (governance, lineage, access controls)

• Cloud platforms: AWS, Azure, or GCP

• Infrastructure as Code (Terraform strongly preferred)

• CI/CD tools (GitHub Actions, Azure DevOps, GitLab, etc.)

• Data formats and protocols (Parquet, JSON, Avro)

Certifications Required:

• Databricks Certified Data Engineer Professional

• Databricks Certified Professional Architect (or equivalent advanced certification)

Preferred / Additional Certifications

• AWS Certified Solutions Architect (Associate or Professional)

• Azure Solutions Architect Expert

• Google Professional Data Engineer

• Databricks Machine Learning Professional

• Snowflake or other cloud data platform certifications

Soft Skills

• Strong architectural decision-making and documentation skills

• Excellent communication with technical and non-technical stakeholders

• Ability to lead design reviews and architecture governance forums

• Strong troubleshooting and performance-tuning mindset

Nice-to-Have Experience

• MLflow and MLOps architectures

• Real-time analytics and streaming pipelines

• Multi-region or cross-account data architectures

• Consulting or MSP delivery experience

Originally posted on Himalayas