Data Engineering Lead (Hybrid

About Rewards Network

For 41 years, Rewards Network has been helping restaurants grow revenue, increase traffic, and boost customer engagement through innovative financial, marketing services, and premier dining rewards programs. By offering unique card-linked offers, we introduce diners to fantastic restaurant experiences, leveraging advanced technology and data analytics to deliver value to restaurants, diners, and our strategic partners' loyalty programs.

At Rewards Network, you'll be part of a driven and diverse team that excels in collaboration, issue resolution, and taking ownership of both personal growth and the company's success. We take pride in partnering with the world's most powerful loyalty programs to drive full-price paying customers to local restaurants through marketing services and flexible funding options. Our engaging and rewarding environment is designed to help you gain your full potential.

The Data Engineering Lead is responsible for driving the modern data engineering foundation at Rewards Network while providing technical leadership across both data engineering and data science functions. The team is mid-migration —actively building on a modern stack— and this role exists to ensure disciplined execution, enforce architectural clarity and consistency.

The right person is a hands‑on player‑coach that is comfortable building teams as we invest in building out this competency as a core pillar of our long-term competitive advantage and strategy. That means assessing the current in-flight build and making deliberate decisions about what to keep and what to replace. Initially, the Data Engineering Lead will also manage the Data Science team, including acting as a key resource to the team, prioritizing work, and clearing roadblocks.

This is a hybrid position that requires in office presence 3 days a week (Tuesday-Thursday) in Chicago.

• Establish and enforce architecture standards that ensure consistent designs that produce repeatable results.
• Assess the current state of our data infrastructure — evaluate what needs refactoring, and what should be replaced — then execute on that plan with the team.
• Lead the continued build-out of the modern data stack
  , including ELT pipelines, stream ingestion, transformation logic along with storage and compute — serving as the technical authority when the team needs a decision made.
• Hire and develop data engineering talent
  , grow the team with engineers experienced in modern ELT architectures and develop the existing team members through mentorship, code review, and technical leadership.
• Define and maintain the data model RFC process
  , reviewing proposed changes, enforcing boundary discipline, and ensuring no undocumented changes promote to canonical layers.
• Directly lead the data science team
  , setting priorities, managing work streams, and translating business problems into clearly scoped DS projects — fostering a culture of accountability, technical rigor, and continuous improvement.
• Own data pipeline monitoring and observability
  , ensuring production pipelines have appropriate alerting, data quality checks, and incident response processes so failures are caught early and resolved quickly.
• Provide regular visibility into team progress, architectural decisions, and risks to senior leadership, communicating tradeoffs clearly and escalating when business commitments are at risk.

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
• 6–10 years of experience in data engineering or a closely related discipline
• 2+ years in a technical lead or staff-level individual contributor role
• Familiarity with data science workflows, ML model lifecycle, and MLOps concepts
• Expertise in data engineering — including model design, testing strategy, incremental patterns, and layer boundary governance
• Experience designing and enforcing data modeling standards in a team environment — not just building models, but establishing the patterns others follow
• Demonstrated ability to bring a team along technically — through…