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

12+ years in data engineering/architecture; 3+ years on Databricks at enterprise scale; Deep Spark expertise (architecture, performance tuning, streaming, debugging); Lakehouse fluency (Medallion architecture, Delta Lake, Unity Catalog, governance, orchestration with Workflows, DLT/Lakeflow, Airflow, ADF); migration experience leading large transformation programs (legacy DW/ETL, Hadoop, on-prem estates); presales instinct with ability to sell, price, and defend solutions; strong Python/SQL/Scala; CI/CD for data platforms; comfort integrating ML/AI (MLflow, LLM APIs like OpenAI and Anthropic); Databricks Data Engineer Professional or SA accreditations strongly preferred.

Own end-to-end architecture and design decisions on Databricks engagements, ensuring secure, scalable, performant solutions aligned with Lakehouse best practices. Lead delivery of production-grade data platforms (ingestion, transformation, orchestration, governance via Unity Catalog, downstream BI/ML enablement). Lead large-scale migrations and cloud transformation projects to Databricks on Azure/AWS/GCP, including assessment, wave planning, and cutover. Stay hands-on with performance tuning, debugging, code and design reviews, and setting engineering standards. Own and defend enterprise solution designs in front of architecture boards and C-level stakeholders; drive presales end-to-end (discovery, solutioning, estimation, PoCs, RFPs, proposals). Translate technical trade-offs into executive-ready decisions.