Design, build, and scale enterprise Databricks Lakehouse platforms. Lead architecture, platform standardization, ingestion frameworks (batch/stream), governance, performance/cost optimization, cloud integration, and mentor engineering teams.
Role Overview
Data Platform Architecture
Mandatory Skills
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.
Data Platform Architecture
- Define and implement end-to-end Databricks Lakehouse architecture.
- Design scalable systems for:
- Batch & real-time data processing
- Structured & unstructured workloads
- Batch & real-time data processing
- Establish medallion architecture (Bronze, Silver, Gold layers) as a standard.
- Lead deployment and optimization of:
- Azure Databricks / AWS Databricks / GCP Databricks
- Azure Databricks / AWS Databricks / GCP Databricks
- Define standards for:
- Workspace design & cluster strategy
- Job orchestration
- Data storage (Delta Lake)
- Workspace design & cluster strategy
- Drive adoption of:
- Unity Catalog
- MLflow
- Databricks SQL & Photon
- Unity Catalog
- Architect robust data ingestion frameworks:
- Batch (ADF, Airflow)
- Streaming (Kafka, Event Hub)
- Batch (ADF, Airflow)
- Define reusable patterns for:
- ETL/ELT pipelines
- Data modeling (star schema, data vault, dimensional models)
- ETL/ELT pipelines
- Guide engineering teams on best practices in Spark/PySpark optimization.
- Optimize workloads for:
- Query performance
- Cluster utilization
- Storage efficiency
- Query performance
- Implement cost governance strategies (auto-scaling, job clusters, spot instances).
- Architect enterprise-grade governance frameworks:
- Data lineage, cataloging, metadata management
- Fine-grained access control (RBAC/ABAC)
- Data lineage, cataloging, metadata management
- Ensure compliance with data privacy and regulatory standards.
- Integrate Databricks with:
- Data sources (ERP, CRM, APIs, IoT)
- BI tools (Power BI, Tableau)
- ML pipelines and AI platforms
- Data sources (ERP, CRM, APIs, IoT)
- Collaborate with cloud architects for:
- Networking, security, and storage strategies.
- Networking, security, and storage strategies.
- 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
- Data Mesh
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
- Databricks (must-have)
- Strong expertise in:
- Delta Lake
- Distributed data processing
- Delta Lake
- Experience with at least one cloud:
- Azure (preferred), AWS, or GCP
- Azure (preferred), AWS, or GCP
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