Staff Data Engineer

Prodege, LLC is a cutting-edge marketing and consumer insights platform seeking a Staff Data Engineer to architect, build, and operate production data systems. This role involves owning core components of a modern data platform and delivering scalable data architecture to support analytics and machine learning across the company.

Responsibilities:

• 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

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
• 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