Data Engineer

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 develop integrated solutions across Foundry focus areas.
• Support the design and delivery of data visualization and analytic tooling (e.g., Power BI, Tableau, Streamlit) for client and internal projects.
• Contribute to documentation and internal guides that capture lessons learned, reusable modules, and technical standards. 

Practice Development 

• Mentor junior engineers and foster adoption of best practices in data architecture, version control, testing, and CI/CD.
• Participate in Scaling Days to share learnings, refine templates, and advance the firm’s reusable engineering assets.
• Identify opportunities to generalize project solutions into reusable Foundry tooling and QuickStarts.
• Contribute to research and evaluation of emerging technologies (e.g., SQLMesh, OpenLineage, LLM data evaluation pipelines).
• Travel 10 – 25% depending on project assignments.

What You'll Bring

• 6+ years of experience in data engineering, data infrastructure, or software engineering roles.
• Deep proficiency in Python, SQL, and modern data stack tools (Airflow, dbt, Spark, Snowflake, BigQuery, or equivalent).
• Strong understanding of cloud architecture and Infrastructure as Code (Terraform, CloudFormation, or similar).
• Experience with version control, CI/CD workflows, and containerization (Docker, Kubernetes).
• Proven ability to architect and optimize large-scale data systems for performance, reliability, and reproducibility.
• Excellent communication skills with the ability to convey technical concepts to non-technical audiences.
• Demonstrated collaboration across disciplines and commitment to mentoring and technical excellence.
• Bachelor’s or advanced degree in Computer Science, Engineering, or related field. 

Preferred Experience

• Prior experience at a consulting firm or matrix-based organization.
• Experience developing internal libraries, frameworks, or automation solutions.
• Familiarity with data governance, lineage tracking, or privacy-preserving techniques.
• Exposure to AI/ML data pipelines, LLM evaluation, or text analytics systems.