Principal Data Engineer

Prodege•El Segundo, CA

7d•$235,000 - $265,000

About The Position

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.

Requirements

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 Haves

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

Responsibilities

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
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