Chief Technology Officer - Databricks Data & AI

Requirements:

• At least 10 years of progressive technology leadership, including 5 or more years as a CTO, VP of Engineering, Chief Architect, or in an equivalent role; consulting, systems-integrator, or partner-services experience; and a record of aligning and influencing senior technical leaders across teams.
• Hands-on experience designing and delivering enterprise-scale production systems on Databricks, including Lakehouse, Delta Lake, Lakeflow, structured streaming, Unity Catalog, and multi-workspace or multi-cloud deployments. Able to review designs, data models, code, performance, and costs in depth.
• Proven success leading migrations from legacy data warehouses and ETL platforms such as Teradata, Netezza, Oracle, SQL Server, Informatica, and SSIS to Databricks, covering assessment, wave planning, code conversion, reconciliation, and cutover, and validating estimates.
• Experience delivering production machine-learning and generative AI systems, as well as AI modernization, agentic analytics, and advanced analytics—such as Agent Bricks, AI/BI Genie, RAG, and predictive models—with measurable business impact.
• Strong knowledge of Apache Spark internals and at least one major cloud platform (AWS, Azure, or Google Cloud Platform); experience with CI/CD using Databricks Asset Bundles, infrastructure as code, MLOps, FinOps, hands-on Python/PySpark and SQL, and governance and security in regulated industries.
• Ability to position Databricks against Snowflake, Microsoft Fabric, BigQuery, and AWS-native analytics; knowledge of interoperability technologies such as Delta Lake, Iceberg, UniForm, and Delta Sharing, along with the broader BI, orchestration, and streaming ecosystem.
• Databricks professional- or architect-level credential, such as Data Engineer Professional or Platform Architect accreditation, or commitment to earn one within six months. ML or GenAI certifications, partner accreditations, and Databricks Champion or MVP status are advantageous.
• Excellent storytelling, presentation, and executive advisory skills, with experience representing an organization to board-level stakeholders, industry analysts, and media.

Responsibilities:

• Advise client executives on long-term data and AI platform strategy, target architecture, governance, and AI readiness; define our practice vision and offerings for AI modernization, agentic analytics, and advanced analytics.
• Establish reference architectures and engineering standards for Lakehouse and medallion architectures, Unity Catalog governance, Lakeflow pipelines, and Mosaic AI, as well as production practices for CI/CD, MLOps, FinOps, and release cycles.
• Lead the technical side of major Databricks opportunities, from discovery workshops and assessments through solution design, effort estimates, RFP responses, SOW development, and executive presentations; review and provide escalation support across engagements.
• Design and lead migrations from legacy data warehouses and ETL platforms to Databricks, and develop reusable migration resources, including assessment templates, conversion accelerators, and reconciliation frameworks.
• Lead our technical relationship with Databricks through co-selling, joint go-to-market activity, and roadmap briefings; strengthen our partner-program standing and coordinate with AWS, Azure, and Google Cloud Platform relationships.
• Build reusable practice assets and offerings, lead public and analyst relations as our primary technical spokesperson, and represent EPAM at conferences, webinars, and Databricks community events.

Technologies:

• AI
• AWS
• Architect
• Azure
• BigQuery
• CI/CD
• Cloud
• CTO
• Databricks
• ETL
• Fabric
• Informatica
• Support
• MLOps
• Oracle
• Python
• PySpark
• RAG
• SQL
• Security
• Snowflake
• Spark
• SSIS
• Unity
• GameDev

More

We are seeking an accomplished technology executive to serve as Chief Technology Officer for our Databricks Data & AI consulting practice. Reporting to our CEO, you will work with client executives, our account and delivery leaders, Databricks field teams, and industry analysts. We measure success through client outcomes, including successful migrations and production-ready data and AI capabilities; practice growth through influenced pipeline, won deals, and adoption of reusable assets; and our Databricks channel and market presence, including co-sell pipeline, partner-program standing, and analyst and media visibility.

last updated 43 week of 2026