Databricks Architect

As a highly experienced Databricks Architect, your role will involve designing, migrating, and optimizing large-scale data platforms on Databricks. You will need strong architectural ownership, hands-on experience with Databricks migrations, and deep expertise in modern data engineering ecosystems.

Key Responsibilities:

• Design and lead end-to-end data architecture solutions using Databricks
• Own and execute data migration strategies into Databricks from legacy or cloud data platforms
• Architect scalable Lakehouse solutions using Delta Lake, Spark, and cloud-native services
• Define best practices for data modeling, ingestion, processing, and governance
• Collaborate with data engineers, analytics teams, and stakeholders to translate business requirements into technical designs
• Optimize performance, reliability, and cost of Databricks workloads
• Ensure data security, compliance, and access controls within Databricks environments
• Review and guide implementation through design reviews and technical governance

Qualifications Required:

• Several years of experience as a Data Architect / Databricks Architect
• Strong hands-on experience with Databricks (Spark, Delta Lake, Lakehouse architecture)
• Proven track record of migrating data platforms into Databricks
• Solid background in data engineering prior to Databricks adoption
• Deep understanding of distributed systems and big data architectures
• Experience with cloud platforms (AWS / Azure / GCP)
• Strong expertise in ETL/ELT pipelines, data modeling, and performance tuning

Good to Have:

• Databricks Certifications (Architect / Data Engineer / Professional level)
• Experience with governance tools (Unity Catalog, data lineage, metadata management)
• Exposure to streaming frameworks and real-time data processing

In this role, you will be working as a senior architect who has designed and owned Databricks-based solutions. You should be comfortable making architectural decisions, guiding teams, and maintaining a strong balance of hands-on expertise and high-level system design in complex, enterprise-scale data environments. As a highly experienced Databricks Architect, your role will involve designing, migrating, and optimizing large-scale data platforms on Databricks. You will need strong architectural ownership, hands-on experience with Databricks migrations, and deep expertise in modern data engineering ecosystems.

Key Responsibilities:

• Design and lead end-to-end data architecture solutions using Databricks
• Own and execute data migration strategies into Databricks from legacy or cloud data platforms
• Architect scalable Lakehouse solutions using Delta Lake, Spark, and cloud-native services
• Define best practices for data modeling, ingestion, processing, and governance
• Collaborate with data engineers, analytics teams, and stakeholders to translate business requirements into technical designs
• Optimize performance, reliability, and cost of Databricks workloads
• Ensure data security, compliance, and access controls within Databricks environments
• Review and guide implementation through design reviews and technical governance

Qualifications Required:

• Several years of experience as a Data Architect / Databricks Architect
• Strong hands-on experience with Databricks (Spark, Delta Lake, Lakehouse architecture)
• Proven track record of migrating data platforms into Databricks
• Solid background in data engineering prior to Databricks adoption
• Deep understanding of distributed systems and big data architectures
• Experience with cloud platforms (AWS / Azure / GCP)
• Strong expertise in ETL/ELT pipelines, data modeling, and performance tuning

Good to Have:

• Databricks Certifications (Architect / Data Engineer / Professional level)
• Experience with governance tools (Unity Catalog, data lineage, metadata management)
• Exposure to streaming frameworks and real-time data processing

In this role, you will be working as a senior architect who has designed and owned Databricks-based solutions. You should be comfortable making architectural decisions, guiding teams, and maintaining a strong balance of hands-on expertise and high-level system design in complex, enterprise-scale data environments.