Chief Architect - Data Engineering (Databricks Practice)

Job Description

Job Description

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