Principal Data Engineer

RAVL helps technologists accelerate their careers.

At RAVL, we connect strategy with execution, care deeply about the people we work with, and measure success by the lasting impact we leave behind. Our purpose is to build a team that puts real, sustainable business outcomes at the core of everything we do.

We’re here to leave our clients better than we found them, and to create a place where our people are proud to Build. Better.

As a Principal Data Engineering leader at RAVL, you will design, define, and elevate enterprise-grade data and AI platforms that are secure, scalable, and high-performance. Operating as a Community of Practice (COP) Leader within the BuildIQ organization, you will build horizontal data and AI capabilities that uplift all Organizations of Engagement (OOEs), providing expert architectural guidance, platform standards, and reusable accelerators across teams.

You will thrive in environments of ambiguity, applying a delivery-first, get-shit-done mentality to make progress visible and impactful. Success at RAVL means combining deep technical mastery in raw data engineering, data platform architecture, and applied AI systems with creativity, openness of mindset, and the ability to influence as a trusted outsider in complex client environments. This role is accountable not just for delivery, but for raising the performance bar of the entire data discipline.

• Design and deliver enterprise data platforms, lakehouse architectures, and distributed raw data processing systems using modern cloud-native technologies.
• Architect and implement scalable batch and streaming pipelines, medallion architectures, data mesh patterns, and platform automation frameworks for resilience, governance, and security.
• Standardize and lead adoption of Databricks, Apache Spark, Delta Lake, and similar distributed data processing ecosystems across engagements.
• Define and implement AI-ready data foundations, including feature engineering pipelines, model-ready data layers, and scalable experimentation environments.
• Build horizontal capabilities including ingestion frameworks, metadata and lineage standards, data quality and observability frameworks, secure-by-design platform blueprints, and MLOps enablement patterns.
• Architect and guide implementation of MLOps workflows including model lifecycle management, model deployment strategies, monitoring, and governance.
• Integrate with cloud-native storage, data warehouses, APIs, ML platforms, vector databases, and enterprise systems while managing authentication, authorization, and secure data flows.
• Apply secure coding practices, compliance standards, responsible AI principles, and automation-first approaches across all data and AI platform designs.
• Demonstrate a bias for action: ship reference architectures, reusable modules, AI accelerators, and templates that enable rapid, incremental delivery.
• Mentor engineers, influence stakeholders, define governance standards, and shape technical and strategic direction across BuildIQ.

Candidates must demonstrate proficiency in all of the following:

• Programming: Advanced Python and SQL, plus Scala or Java
• Data Platform Tooling: Databricks, Apache Spark, Delta Lake
• AI & ML Tooling: Experience with ML frameworks (e.g., MLflow, PyTorch, TensorFlow) and model lifecycle tooling
• Infrastructure & Automation: Terraform and CI/CD pipelines
• Cloud Platforms: Deep expertise in at least one of AWS, Azure, or GCP, with working knowledge of a second
• Security & Governance: IAM, encryption (at rest and in transit), RBAC, secure coding practices, data governance, and responsible AI fundamentals

(Candidates missing these will not be considered further.)