Databricks Architect (Remote, US)

V4C.ai

21h•$170,000 - $220,000•Remote

About The Position

The Data Architect is the most senior technical voice on customer engagements, owning and defending target-state architecture for enterprise Lakehouse platforms. The role defines how data moves, where it lands, how it is governed, and what it costs to operate. Work commonly involves assessing legacy environments—including Teradata, Informatica, Hadoop, and cloud sprawl—designing a migration path to a Databricks Lakehouse, and staying engaged through implementation to ensure the design is realized. The architect works directly with customers and often with Databricks field teams, contributes to engagement scoping and pre-sales discovery, and helps raise the architectural capabilities of the practice. V4C.ai describes itself as a pure-play Databricks partner. It offers technical hires a certification roadmap taught in-house by Databricks-approved trainers and a dedicated Employee Success Manager. The position is listed as remote in the US; the benefits section also mentions working from home three days per week.

Requirements

At least 8 years in data engineering, architecture, or analytics platform development.
Demonstrated ownership of end-to-end architecture for at least two enterprise-scale data platforms, from design through production.
Deep experience migrating legacy data warehouses or ETL estates to a modern cloud platform, such as Teradata, Informatica, Hadoop, or Netezza.
Strong proficiency in Python and SQL.
Working knowledge of Apache Spark and distributed processing fundamentals.
Experience with data modeling across dimensional, normalized, and Lakehouse patterns, and the judgment to determine when each applies.
Experience with CI/CD and DevOps practices for data platform deployment.

Nice To Haves

Healthcare or life sciences experience.
Databricks Certified Data Engineer Professional certification or a Platform Architect certification.
Hands-on Databricks experience at scale, including Unity Catalog, Delta Lake, Lakeflow, and Asset Bundles.
Experience with MLOps or productionizing machine learning workloads.
Prior professional services, consulting, or customer-facing architecture background.
Experience contributing to pre-sales scoping or technical proposals.

Responsibilities

Own target-state Lakehouse architecture on enterprise engagements, including ingestion, storage, transformation, serving, and governance layers.
Lead architecture assessments of legacy estates and produce migration paths covering sequencing, risk, and effort.
Define data modeling standards and Medallion layer conventions, and ensure engineering teams follow them across concurrent workstreams.
Set the Unity Catalog governance model, including catalogs, schemas, access patterns, lineage, and audit posture.
Make and document platform trade-offs involving compute sizing, storage format, streaming versus batch, and build versus buy, supported by cost and performance reasoning for customer stakeholders.
Provide technical input into scoping and effort estimation for new engagements, including pre-sales discovery and proof-of-concept design.
Mentor senior engineers, review designs, and raise the architectural bar across the practice.
Produce reference architectures, diagrams, and internal accelerators to make future engagements faster.