Lead AI-Augmented Data Architect

Data architecture is having a moment — and not everyone is ready for it. The platforms have matured, the AI tooling has caught up, and organizations that invest in the right foundations now will have an analytical edge that compounds for years. This role is the person who builds that foundation.

We're looking for a Senior Global Architect who combines deep platform expertise — Databricks, Snowflake, lakehouse architecture — with a forward-thinking approach to AI-assisted data engineering. You'll design and deliver the enterprise data platform strategy for an elite international law firm with global offices, translating complex financial and operational data into reliable, high-performance analytics that leadership actually trusts.

This is a greenfield opportunity dressed in an enterprise context. The firm is serious about modernizing its data infrastructure, and you'll have the authority and the mandate to do it right — with best-in-class tooling, real executive buy-in, and a team that will listen to you.

The ideal candidate doesn't just know how to model data — they know how to use AI-assisted tools to accelerate pipeline development, auto-generate documentation, surface data quality anomalies, and move faster without cutting corners. Databricks Assistant, Snowflake Copilot, and LLM-assisted SQL and Python development aren't buzzwords here — they're expectations.

• Design and implement enterprise-grade data architectures on Databricks and/or Snowflake, leveraging AI-assisted development tools — Databricks Assistant, Snowflake Copilot, Cursor, Git Hub Copilot — to accelerate pipeline development and reduce time-to-delivery.
• Build efficient, AI-augmented ELT/ETL workflows using Spark, Python, and SQL to ingest, transform, and curate large volumes of financial and operational data — using LLMs to generate boilerplate, suggest optimizations, and auto-document transformation logic.
• Apply AI-driven anomaly detection and data quality tooling to proactively surface reconciliation issues before they reach reporting consumers.
• Evaluate and adopt emerging data platform capabilities — including generative AI features native to Databricks and Snowflake — as part of a continuous platform improvement mandate.

• Create and maintain dimensional, normalized, and semantic data models — star/snowflake schemas, 3NF, and data vault — to serve financial reporting, performance analytics, and forecasting use cases at global scale.
• Lead integration design for Elite 3E, HRIS, and other core source systems, ensuring accurate reconciliation and tight alignment with enterprise warehouse schemas.
• Enforce data governance policies, lineage tracking, and metadata standards across the platform — reducing time-to-insight while ensuring data consumers can trust what they're looking at.
• Establish reusable model patterns and semantic layers that empower BI developers and finance analysts to self-serve without reinventing the wheel.

• Tune query performance, optimize compute and storage costs, and implement caching, partitioning, and clustering strategies — using AI-assisted profiling tools to identify bottlenecks faster.
• Design for scale from day one: lakehouse architecture, Delta Lake, data tiering, and workload isolation across real-time and batch analytical workloads.
• Drive measurable reductions in time-to-insight for finance and leadership teams through optimized pipelines and curated, pre-aggregated data models.

• Partner closely with data engineers, BI developers, finance analysts, and IT leadership to translate business requirements into scalable, well-documented data solutions.
• Influence and align technical direction across teams — you can walk a CFO through a data model and a data engineer through a medallion architecture in the same afternoon.
• Evaluate new tools, frameworks, and architectural patterns continuously, bringing recommendations with clear tradeoffs rather than just enthusiasm.

• 8+ years in data architecture,…