Data Engineer
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About the Role
Keystone is a premier economics, technology, and strategy consulting firm built to help companies lead through transformation. As breakthrough innovations reshape industries, redefine competition and change our society, complex and highly competitive ecosystems emerge. Keystone advises technology leaders, Fortune 100 companies, their legal counsel, and governments on business, economic, litigation, and regulatory strategy in relation to these innovations and competitive eco-systems. We operate globally from offices in New York City, Boston, San Francisco, Seattle, London, Dubai, and Washington, D.C.
K.ATS Foundry is Keystone’s engineering center of excellence, embedding data, platform, and forensic expertise into the firm’s most complex and high-impact projects. Foundry builds secure, reusable infrastructure and scalable technical solutions that accelerate project delivery and ensure defensible, data-driven outcomes. Our engineers work across disciplines—from automating data pipelines and managing cloud platforms to conducting forensic code investigations—helping every engagement start faster, run smoother, and deliver greater impact.
About the Data Engineer Role
You will lead the design and implementation of reproducible, scalable data workflows that power Keystone’s most data-intensive projects. You will architect infrastructure, guide technical implementation across engagements, and develop reusable frameworks that accelerate work across the firm.
This role sits at the intersection of data engineering, platform design, and applied analytics, supporting cross-disciplinary teams in building data systems that are robust, defensible, and automation-first. You’ll serve as a senior individual contributor and mentor, own project-level architecture, shape technical standards, and contribute to Foundry’s shared engineering culture.
Key Responsibilities
Technical Leadership
- Architect and implement end-to-end data pipelines, transformations, and infrastructure across cloud environments (AWS, GCP, Azure, Snowflake).
- Develop reproducible workflows using Infrastructure as Code and data orchestration tools (Airflow, dbt, Spark, or equivalent).
- Design data models and storage systems optimized for performance, scalability, and defensibility in regulatory and litigation contexts.
- Build and maintain reusable templates, libraries, and automation frameworks that improve engineering efficiency across projects.
- Conduct code reviews, mentor peers, and ensure technical rigor and reproducibility in all project deliverables.
Collaboration and Delivery
- Partner with client-facing teams, economists, and data scientists to translate analytical goals into scalable, automated data solutions.
- Collaborate with Platform and Forensic Engineering peers to
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