Data Engineering Practice Lead

Location: Cincinnati or Dallas (not a remote position)

About the role

We are looking for a Data Engineer to lead the continued optimization and maturation of our existing large-scale data platform. This is a hands-on technical leadership role: roughly 60% of your time will be spent as a working solution architect—improving, rearchitecting, and scaling data pipelines, infrastructure, and models that power the business. The remaining 40% will be focused on strategic alignment, technical direction, and resource management for the Data Engineering team. A key part of the mandate is ensuring the platform remains AI/ML ready as it scales—well modeled and well governed so that advanced analytics teams can reliably and efficiently access high-quality data. Data Vault 2.0 or Ensemble data modeling techniques is preferred.

Reporting to the VP of Data Engineering, AI & ML, you will lead a team of Data Engineers—setting the technical execution direction while staying close enough to the architecture and code to guide hard decisions, unblock the team, and ensure the platform continues to scale reliably and securely as usage and AI/ML initiatives grow.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over work authorization sponsorship now or in the future for this position.

Responsibilities

• Own the technical evolution of our existing large-scale data platform, identifying and driving improvements to architecture, performance, and scalability as data volume and complexity grow.
• Act as the lead solution architect for data engineering initiatives.
• Personally design, prototype, and develop frameworks and critical platform components—especially for high-risk/high-complexity initiatives.
• Maintain a forward-thinking posture to create an AI/ML-ready data platform.
• Participate in the evaluation, selection, and implementation of data platform tooling (e.g., storage, orchestration, compute, streaming).
• Reinforce existing data observability solutions and standards across pipelines and the platform.
• Diagnose and resolve complex production issues and technical debt across the data platform—partnering directly with other engineers to resolve.
• Partner with the VP of Data Engineering, AI & ML to define and execute the roadmap for a fully optimized and scalable data platform—ensuring alignment with broader company and data strategies, including AI/ML readiness assessments.
• Set technical direction and architectural standards for the team by working closely with Enterprise Architecture.
• Manage, mentor, and develop data engineers.
• Participate in resource planning and prioritization across the team’s product roadmap—balancing business requests with platform-based initiatives.
• Represent Data Engineering in planning, budgeting, and resource management discussions as needed.
• Stay current with emerging trends in data engineering and data platform architecture—bringing innovations into continued data maturity for the organization.

Requirements

• Bachelor’s degree in Computer Science or related field or equivalent work experience.
• 10+ years of experience in data engineering with significant experience in optimizing, scaling, and evolving large-scale mature data platforms.
• 3+ years in an architect role (Solution, Enterprise, Data, Platform) with direct ownership of solution designs.
• 3+ years in a leadership role managing data engineering teams—including direct people management responsibilities.
• Working knowledge of how data platforms enable AI/ML solutions.
• Track record of balancing hands-on technical delivery with people leadership and strategic planning.

Skills

Technical Knowledge

• Deep expertise in data pipeline design, optimization, and distributed data processing architectures with experience identifying and resolving performance and scalability bottlenecks.
• Hands-on experience with Snowflake or Databricks (experience in an architecture role with these platforms is a plus).
• Experience with Azure Synapse Analytics/MS Fabric.
• Strong knowledge of cloud data platforms (AWS, Azure, or GCP) and modern data warehouse/Lakehouse architectures (Delta Lake, Iceberg).
• Working familiarity with AI/ML data requirements and tooling to support AI/ML enablement.
• Strong working proficiency with at least two programming languages (Python, Scala, SQL).
• Solid understanding of data governance, security, and compliance practices in a regulated or enterprise environment.
• Experience with IaC and CI/CD practices for data systems (e.g., Terraform, GitHub Actions).

Leadership & Soft Skills

• Proven ability to set technical direction while remaining hands-on and credible at the architecture and code level.
• Strong stakeholder management skills—the ability to translate between technical and non-technical audiences.
• Excellent prioritization and resource management skills across competing technical and business demands.
• A coaching mindset with genuine investment in growing the technical and soft skills of direct reports.

Nice to Have

• Experience designing or implementing Agentic workflows for data engineering.
• Experience working with Property & Casualty insurance carriers.
• Experience with Data Vault 2.0 or Ensemble data modeling techniques.