Staff Data Engineer

Prodege•Remote California, CA

12d•$185,000 - $220,000

About The Position

This is a role for an engineer who wants to own core components of a modern data platform. We are looking for a Staff Data Engineer to architect, build, and operate production data systems across Prodege. This is a deeply hands-on technical role. You lead by building production-grade systems, setting engineering standards, and delivering scalable data architecture, not by working strictly at an abstract planning level. You will build and evolve platform capabilities for a business serving over 120 million registered users. Operating within a high-scale data environment featuring a 400 Terabyte footprint, a 100 Terabyte Iceberg lake, 50 million daily events, and 500 million pipeline records, you will deliver the data foundations that power analytics, experimentation, machine learning, and AI-driven decision making across all Prodege products. If you enjoy building distributed systems at scale, working closely with cross-functional technical teams, and driving an AI-first engineering strategy, this role is for you. Prodege is a cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty and product adoption. Bolstered by a major investment by Blackstone in the first quarter of 2026, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.

Requirements

Five to eight or more years of hands-on data engineering experience building large-scale data systems, ideally in advertising technology, marketing technology, consumer internet, or high-volume marketplace environments
Advanced technical expertise with SQL, Python, Snowflake, and dbt
Demonstrated background designing, deploying, and operating production-grade batch, ELT, and streaming pipeline architectures
In-depth understanding of modern data architecture paradigms, including Medallion architecture, data contracts, schema evolution, event-driven systems, and data modeling
Practical experience building data systems that directly support analytics, experimentation platforms, and machine learning workloads
Proven ability to optimize data pipelines and storage systems for scale, reliability, throughput, and cost performance
Strong communication and technical leadership skills, with a track record of driving technical decisions through design reviews, code reviews, and cross-functional alignment

Nice To Haves

Experience with Iceberg, Trino, Kafka, Flink, Kinesis, Spark, or related lakehouse and distributed streaming frameworks
Experience building ML feature pipelines, feature stores, or model training data infrastructure
Experience architecting self-service analytics frameworks or experimentation platforms
Experience with modern DataOps methodologies, workflow orchestration, and data observability tools
Experience leveraging modern AI-assisted software development tools to enhance engineering productivity

Responsibilities

Architecture, implementation, and operational reliability of major data platform domains, including pipelines, modeling layers, and data services
High-scale batch, ELT, and near-real-time streaming pipelines powering business intelligence, machine learning, experimentation, and product analytics
Platform standards for data governance, schema evolution, data contracts, lineage, and end-to-end observability
Data infrastructure and feature pipelines supporting machine learning, experimentation frameworks, and AI-driven applications
Technical quality and engineering standards across the team through direct code contributions, architecture design reviews, and technical mentorship
Architect, build, and operate high-capacity batch, ELT, event-driven, and near-real-time streaming data pipelines
Construct production-grade data platform components utilizing Snowflake, dbt, Iceberg, Trino, Kafka, and modern lakehouse technologies
Design scalable data models adhering to Medallion architecture principles to support business intelligence, advanced analytics, and machine learning
Enforce platform discipline around data contracts, schema evolution, lineage tracking, access governance, and system observability
Optimize data infrastructure for performance, query speed, system reliability, availability, and cost efficiency
Partner cross-functionally with Engineering, Product, Analytics, Business Intelligence, and Machine Learning teams to deliver trusted data foundations
Apply AI-assisted engineering tools to accelerate development velocity, automated testing, system debugging, and technical documentation