Our client, a market-leading company in the legal services industry specializing in bankruptcy and insolvency management, is building a brand-new data platform from the ground up. The organization sits on a large volume of public legal precedents and historical case records, and turning that information into reliable, usable data is central to its growth strategy. This is a foundational hire: the person who takes this role will shape how data is captured, modeled, and delivered across the company from day one.

This role covers the full lifecycle of the platform, from architecture and prototyping to hands-on implementation, delivery, and ongoing support. It requires a rare mix of deep technical execution and architectural ownership, combined with the communication skills to propose, present, and defend technical decisions directly to client leadership. The person will operate within a small, AI-native delivery team — working closely with an AI architecture counterpart and the client's product owner rather than a large traditional team structure.

Responsibilities

Architecture & Platform Build

• Design and build a scalable lakehouse data platform from the ground up, covering everything from raw data ingestion to the layers that serve downstream consumption.
• Define the end-to-end data lifecycle architecture needed to support analytics, machine learning, and business intelligence use cases further down the pipeline.

Hands-on Engineering

• Build production-grade data pipelines, frameworks, and platform services by writing and reviewing code directly, not only specifying requirements for others.
• Lead data cleaning, transformation, and entity resolution work by converting large volumes of unstructured legal and historical records into consistent, trustworthy datasets.

Infrastructure

• Provision, manage, and scale cloud infrastructure as code to enable repeatable, reliable platform deployments.

Technical Leadership

• Act as the primary technical reference for the platform by setting engineering best practices, code quality standards, and architectural guardrails for the team.
• Mentor engineering team members on data engineering practices and platform standards as the team and platform grow.

Stakeholder Engagement

• Partner directly with client leadership and business stakeholders by translating business requirements into technical solutions, and by proposing, presenting, and defending architectural decisions in direct conversations.
• Collaborate closely with adjacent roles, including AI architecture and product ownership, within a lean, AI-native delivery team.

Requirements

• Extensive, hands-on experience designing and building enterprise-grade data platforms from scratch, operating simultaneously as architect and individual contributor.
• Deep expertise in Databricks and lakehouse architecture design.
• Experience with at least one major cloud platform is mandatory (AWS, Azure or GCP).
• Solid proficiency in Infrastructure as Code, using tools such as Terraform, OpenTofu, or AWS CDK.
• Strong command of data engineering fundamentals: data modeling, distributed computing, ETL/ELT design patterns, and entity resolution.
• Demonstrated ability to structure data platforms so they support downstream data science, machine learning, and business intelligence needs.
• Core background rooted in data engineering and pipeline/platform development, rather than analytics or reporting-tool work alone.
• Advanced English proficiency (written and spoken), used daily for technical and strategic communication with client leadership based in the United States.
• Strong communication skills, with proven ability to propose, present, and defend technical solutions directly to client stakeholders.

Nice to Have

• Experience with AWS specifically, as it is the target cloud provider for this Databricks deployment.
• Databricks certification(s).
• Experience working in legal services, financial services, or other document- and precedent-intensive industries.
• Experience operating within small, AI-native delivery teams rather than large traditional team structures.
• Familiarity with AI/ML platform architecture practices, given close collaboration with an AI architecture counterpart.