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.