Role Overview
We are looking for a highly skilled Databricks Architect to design, build, and scale enterprise-grade Lakehouse data platforms. This role will drive architecture strategy, platform standardization, and enterprise data modernization initiatives, leveraging Databricks and cloud ecosystems. The ideal candidate brings deep expertise in Spark, Delta Lake, and cloud-native architecture, along with strong leadership in driving large-scale data transformations.
Key Responsibilities
- Data Platform Architecture Define and implement end-to-end Databricks Lakehouse architecture.
- Design scalable systems for: Batch & real-time data processing Structured & unstructured workloads Establish medallion architecture (Bronze, Silver, Gold layers) as a standard.
- Databricks Platform Leadership Lead deployment and optimization of: Azure Databricks / AWS Databricks / GCP Databricks Define standards for: Workspace design & cluster strategy Job orchestration Data storage (Delta Lake) Drive adoption of: Unity Catalog MLflow Databricks SQL & Photon Solution Design & Engineering Architect robust data ingestion frameworks: Batch (ADF, Airflow) Streaming (Kafka, Event Hub) Define reusable patterns for: ETL/ELT pipelines Data modeling (star schema, data vault, dimensional models) Guide engineering teams on best practices in Spark/PySpark optimization.
- Performance & Cost Optimization Optimize workloads for: Query performance Cluster utilization Storage efficiency Implement cost governance strategies (auto-scaling, job clusters, spot instances).
- Data Governance & Security Architect enterprise-grade governance frameworks: Data lineage, cataloging, metadata management Fine-grained access control (RBAC/ABAC) Ensure compliance with data privacy and regulatory standards.
- Cloud & Ecosystem Integration Integrate Databricks with: Data sources (ERP, CRM, APIs, IoT) BI tools (Power BI, Tableau) ML pipelines and AI platforms Collaborate with cloud architects for: Networking, security, and storage strategies.
- Leadership & Mentorship Provide architectural guidance to data engineers, scientists, and TPMs.
- Conduct design reviews and enforce architecture governance.
- Mentor teams on emerging patterns: Data Mesh DataOps / MLOps GenAI workloads on Databricks Skills & Qualifications Mandatory Skills 12+ years of experience in data engineering, architecture, or platform design.
- 5+ years of hands-on experience with: Databricks (must-have) Apache Spark / PySpark / SQL Strong expertise in: Delta Lake Distributed data processing Experience with at least one cloud: Azure (preferred), AWS, or GCP
