Data Engineering Practice Lead

Cincinnati or Dallas (not a remote position)

We are looking for a Data Engineer, to lead the continued optimization and maturation of our existing large-scale data platform.
This is hands-on t
echnical 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-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 decision, unblock the team, and ensure the platform continues to scale reliably and security 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.

• Own the technical evolution of our existing large-scale data platform, identifying and riving improvements to architecture, performance and scalability data volume and complexity use cases grow
• Act as the lead solution architect for data engineering initiatives
• Personally design, prototype, and development of frameworks and critical platform components – especially for high-risk/high complexity initiatives
• Have a forward-thinking posture as it comes to creating an AI/ML ready data platform
• Participate in the evaluation, selection and implementation of data platform tooling (i.e. – storage, orchestration, compute, streaming)
• Reinforce existing data observability solutions/standards across pipelines and the platform
• Diagnose and resolve complex production issues/technical debt across the data platform – partnering directly with other engineers to resolve.

Strategy, Technical Direction & Resource Management

• Partner with VP-Data Engineering, AI & ML to define and execute the roadmap for a fully optimized and scalable data platform – ensuring alignment to 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 planning, budgeting, and resource management discussions as needed
• Stay current with emerging trends in data engineering and data platform architecture – brining innovations into continued data maturity for the organization.

Technical Knowledge and Understanding:

Technical Knowledge

• Deep expertise in data pipeline design, optimization, and distributed data processing architectures with experience identifying and resolving performance and scalability bottlenecks
• Platforms:
  Hands on experience with Snowflake or Databricks (experience in an architecture role with these platforms is a plus), 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 (i.e. Terraform, Git Hub 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…