Principal Data Engineer

Prodege, LLC is seeking a Principal Data Engineer to shape the future of its data platform. The role is responsible for owning the data stack end to end, building and modernizing high-scale pipelines and platform architecture that support analytics, experimentation, machine learning, business intelligence, and revenue-driving decision-making.

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

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
• • 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