Data Engineering Lead (Hybrid)

Socket.dev

Chicago (IL)

Hybrid

USD 190,000 - 220,000

Full time

14 days+

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Benefits offered by this job

Hybrid work environment with in-office

Competitive benefits

401(k) with company match

Company-paid life insurance

Job summary

Rewards Network is seeking a Data Engineering Lead to drive a modern data foundation, mentor teams, and guide architectural decisions. The role combines hands-on leadership with strategy, overseeing both data engineering and data science activity. A hybrid in-office presence is required in Chicago three days a week.

The ideal candidate has 6–10 years in data engineering, strong Python, Airflow, Kafka, and data modeling expertise, and a track record in guiding teams through complex migrations.

Qualifications

Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience).
6–10 years of experience in data engineering or 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 data modeling standards in a team environment.
Ability to lead technically through code reviews, mentorship, and setting standards.

Responsibilities

Establish and enforce architecture standards that ensure repeatable results.
Assess current data infrastructure and plan refactoring or replacement with the team.
Lead build-out of modern data stack including ELT pipelines and streaming ingestion.
Hire and develop data engineering talent and mentor team members.
Define and maintain data model RFC processes and enforce boundary discipline.
Directly lead the data science team, setting priorities and removing roadblocks.
Own data pipeline monitoring and observability with alerting and quality checks.
Provide regular visibility into progress and risks to senior leadership.

Skills

Python

Apache Airflow

Apache Kafka

Data modeling standards

CI/CD for data pipelines

Data observability

Team leadership

Data science familiarity

Education

Bachelor's degree in Computer Science or related field

Advanced degree in technical field

Tools

GitLab

Job description

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.

Our Culture

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.

Job Overview

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.

What you’ll bring to the table: (Responsibilities)

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 workstreams, 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.

Do you have the right mix of ingredients: (Requirements)

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 code review, documentation, mentorship, and setting standards that stick
Familiarity with CI/CD for data pipelines (GitLab or equivalent) — including automated testing, deployment workflows, and environment promotion strategies
Experience with data observability and monitoring — alerting on pipeline failures, data quality degradation, and freshness SLAs (familiarity with tools like Elementary, Monte Carlo, or equivalent)
Strong decision-making under ambiguity — this role requires someone who can move forward with incomplete information and course-correct, not someone who needs consensus to proceed
Clear, direct communicator who can translate technical tradeoffs for non-technical stakeholders
Technical expertise in: Python, Apache Airflow, Apache Kafka, cloud data warehouses (Redshift, GBQ, Databricks)

Education, Certifications and Experience: Preferred

Advanced degree in a technical field
Experience at a company that has completed a similar legacy-to-modern warehouse migration
Prior experience managing or mentoring data engineers

What you’ll love about us:

Comprehensive benefits package, which includes:

This is a full-time, exempt position. The base salary range for this role in Chicago is $190,000–$220,000 annually, depending on candidate's experience, as well as skills, and other factors. This role is also eligible for an annual bonus target of 15%, bringing total target compensation to $218,500–$253,000.
Competitive Time Off Benefits: including flexible PTO, 11 company holidays, and parental leave.
Generous dining reimbursement when you dine with our restaurant clients
401(k) plan with a company match
Two medical plan options- Standard PPO or High Deductible Health Plan (HSA with company match for HDHP participants)
Partnership with Rx n Go, offering certain prescriptions for free
Two dental plan options and a vision plan
Flexible Spending Accounts and a pre-tax commuter benefit program
Accident, Critical Illness, and Hospital Indemnity Insurance Plans
Short Term and Long Term disability
Company-paid life insurance and AD&D insurance, supplemental employee, spouse, and child life insurance
Employee Life Assistance Program
Hybrid working environment in a new office space downtown near the Metra Train stations and catered lunches on Tuesdays.