Job description

Job Description: Strategic Imperative: We are looking for a Principal Data Engineer to shape the future of data at Prodege.This is a high-impact role for someone who wants to own more than pipelines. You will help build the next-generation data platform that powers analytics, experimentation, machine learning, business intelligence, and revenue-driving decision-making across Prodege’s products and business domains. In this role, you will own the data stack end to end — from platform architecture and data modeling, to pipeline delivery, governance, observability, and the business outcomes those systems enable. You will work across a complex ecosystem spanning owned and operated brands, performance marketing, rewards, customer experience, and machine learning, helping the company scale faster on a more trusted, modern, and AI-ready data foundation. You will help build the next-generation data platform supporting a business serving 120M registered users that has delivered $2B in lifetime rewards, operating at real engineering scale with 400TB total data footprint, 100TB Iceberg lake and growing, 50M raw events per day, 500M records of daily pipeline throughput, 50 Kafka topics and growing, and 300K queries per day across multiple engines. This is a deeply hands-on principal role. We are looking for someone who leads by building, shipping, and modernizing production data systems — not someone who stays only at the architecture or strategy layer. If you enjoy solving hard data problems, building durable platforms, and helping teams move faster with better foundations, this role is for you. What You’ll Own The architecture and evolution of the next-generation data platform / lakehouse High-scale batch, ELT, and near-real-time data pipelines that power BI, experimentation, ML, and product systems Trusted data foundations across Snowflake, dbt, Iceberg, Trino, streaming, event-driven systems, and similar modern data technologies Platform patterns for Medallion architecture, data contracts, schema evolution, governance, and observability Data foundations that support machine learning, feature pipelines, experimentation, and decisioning Hands-on technical leadership across the Data Engineering organization through direct contribution, design reviews, and mentoring The evolution of Data Engineering toward a more AI-first way of working What Makes This Role Exciting You will build the foundation that powers analytics, BI, experimentation, and machine learning across the company You will own data from architecture through production outcome, not just pipeline delivery You will work across a complex ecosystem spanning owned brands, rewards, performance marketing, customer experience, and ML You will help power a business serving 120M registered users that has delivered $2B in lifetime rewards You will work on a real production platform at scale: 400TB data footprint, 100TB Iceberg lake, 50M daily events, 500M daily pipeline records, 50 Kafka topics and growing, and 300K daily queries You will have principal-level scope to influence platform design, standards, and the future of data engineering across the company You will help push the data organization toward a more AI-first engineering future What You’ll Do Lead the design, build, and evolution of the next-generation data platform across Snowflake, dbt, Iceberg, Trino, Kafka, and related technologies Personally drive critical implementations in the data platform, modernizing legacy pipelines and proving out new approaches before scaling them across the team Build and optimize high-scale batch, ELT, and near-real-time pipelines for BI, product, experimentation, and ML use cases Establish durable platform patterns around Medallion architecture, data contracts, schema management, lineage, observability, and governance Design and evolve scalable data models and data marts that support business reporting, self-service analytics, and ML workloads Make key decisions on tooling, orchestration, storage patterns, performance, reliability, and cost efficiency Partner closely with ML, BI, Product, Engineering, Analytics, and business stakeholders to turn business needs into scalable technical designs Build data foundations that support model training, feature pipelines, experimentation, and AI-driven applications Drive an AI-first mindset by using AI to accelerate development, debugging, design exploration, testing, and documentation Mentor data engineers and raise the bar on technical quality, maintainability, and engineering discipline Must Have 6 years of hands-on experience in Data Engineering, ideally in AdTech, MarTech, Growth, consumer internet, or other high-scale / multi-product environments Strong hands-on expertise in SQL, Python, Snowflake, and dbt Proven experience designing and building modern data platforms at scale Strong experience with: batch and near-real-time data pipelines streaming / event-driven architectures modern data modeling and ELT patterns Medallion architecture data contracts and schema evolution Proven experience modernizing complex, interconnected data systems with strong ownership Experience building production-grade data systems that support BI, experimentation, product analytics, and ML use cases Strong judgment across performance, scalability, reliability, and cost tradeoffs Experience partnering cross-functionally with Data Science, BI, Product, Engineering, and business teams Ability to guide teams toward an AI-first way of working, while maintaining strong validation and engineering discipline Strong technical leadership and mentoring capability, with the ability to influence across teams without direct authority Comfort operating in ambiguity and still driving systems into production Nice to Have Experience with Iceberg, Trino, Kafka, Kinesis, Apache Flink, or similar modern lakehouse / streaming technologies Familiarity with feature stores, model-serving pipelines, and DataOps practices Experience supporting experimentation platforms, self-serve BI, or performance marketing use cases Experience in consumer rewards, surveys, monetization, or marketplace-style ecosystems Experience with cloud-native data stacks and modern observability tooling Familiarity with AI-assisted or AI-first development practices across data teams The anticipated base salary range for this position is $235,000 to $265,000. The final salary offered to a successful candidate will be dependent on several factors that may include, but are not limited to; the type and length of experience within the job, type and length of experience within the industry, the type and length of knowledge and skills for the position, education, training, etc. Prodege is a multi-state employer and final compensation within this range could be impacted by work location. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Prodege Benefits: Prodege offers a comprehensive benefits package to US Full-time employees including medical, dental, vision, STD, LTD and basic life insurance. Employees receive flexible PTO, as well as paid sick leave prorated based on hire date. US Employees have eight paid holidays throughout the calendar year. Equal Employment Opportunity Statement At Prodege, we are committed to creating a diverse and inclusive environment. We are proud to be an Equal Opportunity Employer and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. We encourage individuals of all backgrounds to apply. FCIHO Employers will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of FCIHO.

Requirements

Must-haves

What You’ll Own
The architecture and evolution of the next-generation data platform / lakehouse
High-scale batch, ELT, and near-real-time data pipelines that power BI, experimentation, ML, and product systems
Trusted data foundations across Snowflake, dbt, Iceberg, Trino, streaming, event-driven systems, and similar modern data technologies
Platform patterns for Medallion architecture, data contracts, schema evolution, governance, and observability
Data foundations that support machine learning, feature pipelines, experimentation, and decisioning
Hands-on technical leadership across the Data Engineering organization through direct contribution, design reviews, and mentoring
The evolution of Data Engineering toward a more AI-first way of working
What Makes This Role Exciting You will build the foundation that powers analytics, BI, experimentation, and machine learning across the company
You will own data from architecture through production outcome, not just pipeline delivery
You will work across a complex ecosystem spanning owned brands, rewards, performance marketing, customer experience, and ML
You will help power a business serving 120M registered users that has delivered $2B in lifetime rewards
You will work on a real production platform at scale: 400TB data footprint, 100TB Iceberg lake, 50M daily events, 500M daily pipeline records, 50 Kafka topics and growing, and 300K daily queries
You will have principal-level scope to influence platform design, standards, and the future of data engineering across the company
You will help push the data organization toward a more AI-first engineering future
What You’ll Do Lead the design, build, and evolution of the next-generation data platform across Snowflake, dbt, Iceberg, Trino, Kafka, and related technologies
Personally drive critical implementations in the data platform, modernizing legacy pipelines and proving out new approaches before scaling them across the team
Build and optimize high-scale batch, ELT, and near-real-time pipelines for BI, product, experimentation, and ML use cases
Establish durable platform patterns around Medallion architecture, data contracts, schema management, lineage, observability, and governance
Design and evolve scalable data models and data marts that support business reporting, self-service analytics, and ML workloads
Make key decisions on tooling, orchestration, storage patterns, performance, reliability, and cost efficiency
Partner closely with ML, BI, Product, Engineering, Analytics, and business stakeholders to turn business needs into scalable technical designs
Build data foundations that support model training, feature pipelines, experimentation, and AI-driven applications

Nice-to-haves

Nice to Have Experience with Iceberg, Trino, Kafka, Kinesis, Apache Flink, or similar modern lakehouse / streaming technologies

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlsqlpython

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

Pto