Chief Architect - Data Engineering (Databricks Practice)

We are building a fast-growing Databricks practice delivering enterprise data and AI solutions across APAC. As Lead Solution Architect - Data Engineering, you will be the technical anchor of the practice: leading solution design on client engagements, owning and defending enterprise architectures in presales, and driving large-scale migration and cloud transformation programs. This is a hands-on leadership role for a builder who can equally command a whiteboard in front of a CTO and a Spark UI when a pipeline misbehaves. You will also shape the practice itself — mentoring engineers, creating accelerators and reusable assets, and converting delivery success into case studies and go-to-market offerings. Architecture & Delivery • Own end-to-end architecture and design decisions on Databricks engagements, ensuring solutions are secure, scalable, performant, and aligned with Lakehouse best practices • Lead delivery of production-grade data platforms — ingestion, transformation, orchestration, governance through Unity Catalog, and downstream BI/ML enablement • Lead large-scale migrations (legacy DW/ETL, Hadoop, on-prem estates) and cloud transformation projects to Databricks on Azure/AWS/GCP, including assessment, wave planning, and cutover • Stay hands-on: performance tuning, debugging, code and design reviews, and setting engineering standards for the team Presales & Stakeholder Management • Own and defend enterprise solution designs in front of architecture boards, CIOs, and CTOs — and enjoy it • Drive presales end-to-end: discovery, solutioning, estimation, PoCs, RFPs, and proposals that convert • Translate hard technical trade-offs into decisions executives can act on — from engineer to boardroom without changing gears Who Thrives Here • A builder at heart — 12+ years in data engineering/architecture, 3+ on Databricks at enterprise scale, and still happiest when hands are on the keyboard • Deep Spark expertise — architecture, performance tuning, streaming, debugging, the advanced stuff that separates architects from diagram-drawers • Lakehouse fluency — Medallion architecture, Delta Lake, Unity Catalog, governance, orchestration (Workflows, DLT/Lakeflow, Airflow, ADF); Microsoft Fabric exposure a bonus • Battle-tested in migrations — you've led large transformation programs and have the scars and success stories to show for it • Presales instinct — you don't just design solutions; you sell them, price them, and defend them under fire • Modern edge — strong Python/SQL/Scala, CI/CD for data platforms, and comfort integrating ML/AI (MLflow, LLM APIs like OpenAI and Anthropic) into what you build • Certified credibility — Databricks Data Engineer Professional / SA accreditations strongly preferred • Founder energy — curiosity, adaptability, and the drive to build offerings, not just deliver projects Show more Show less

12+ years in data engineering/architecture, 3+ years on Databricks at enterprise scale. Hands-on practitioner with strong Python/SQL/Scala, performance tuning, and ability to review code and designs. Databricks Data Engineer Professional or SA accreditation strongly preferred.

Own end-to-end architecture and design decisions on Databricks engagements; lead delivery of production-grade data platforms (ingestion, transformation, orchestration, governance with Unity Catalog, downstream BI/ML enablement); lead large-scale migrations and cloud transformation projects to Databricks on Azure/AWS/GCP; stay hands-on with performance tuning and engineering standards; own and defend enterprise solution designs in front of architecture boards and senior stakeholders; drive presales from discovery to PoCs and proposals; translate technical trade-offs for executive audiences; contribute to building accelerators and reusable assets for the practice; mentor engineers and shape go-to-market offerings.