QBurst Technologies
From QBurst Technologies's careers page
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Lead enterprise pre-sales engagements, including discovery workshops, solution architecture design, technical proposals, and executive presentations to CTOs, CDOs, and engineering leadership
Design and define modern Lakehouse architectures on Databricks, leveraging Delta Lake, Delta Live Tables, Unity Catalog, Databricks SQL, MLflow, and related platform capabilities
Own the technical direction of delivery engagements and lead a team of 4–8 data engineers by conducting design reviews, code reviews, and enforcing quality standards
Collaborate with account managers and practice leaders to qualify opportunities, estimate effort, define delivery approaches, and support commercial proposals
Assess customer data platforms, identify capability gaps, and develop migration or modernization roadmaps
Establish governance, security, and compliance frameworks, including data lineage, auditability, access controls, and data quality practices
Develop reusable accelerators, reference architectures, delivery frameworks, and best practices to improve delivery efficiency and consistency
Represent the organization as a Databricks and Lakehouse subject matter expert at customer engagements, partner forums, industry events, and technical communities
Mentor and coach data engineers and architects, contributing to capability development across the practice
10+ years of hands-on experience in data engineering and data platform development
5+ years of production experience with Databricks, including Unity Catalog, Delta Lake, Spark Structured Streaming, Databricks Workflows, and Databricks SQL
Proven experience designing and delivering enterprise-scale Lakehouse solutions supporting large-scale data environments and governance requirements
Strong programming skills in Python and/or Scala for Spark-based workloads, with advanced SQL proficiency
Experience working directly with enterprise customers in consulting, professional services, or system integration environments
Demonstrated ability to lead workshops, create Statements of Work (SOWs), define solution architectures, and engage senior business and technology stakeholders
Deep understanding of data governance, security, lineage, auditability, and regulatory compliance within modern data platforms
Databricks Certified Data Engineer Professional and/or Databricks Certified Associate Developer for Apache Spark certification
Experience implementing Databricks across AWS, Azure, and GCP environments
Familiarity with the broader data ecosystem, including dbt, Fivetran, Tableau, Power BI, and related technologies
Experience with Databricks Feature Store, MLflow, and ML-integrated data engineering workflows
Prior experience in a Senior Architect, Principal Architect, or similar leadership role within a consulting organization, systems integrator, or Databricks partner
Contributions to the data engineering community through technical blogs, conference presentations, open-source projects, or thought leadership content
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