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.
What does success look like in this role?
- 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.
Sounds great, do my skills fit?
- Distributed data processing and Spark internals
- Lakehouse architecture and medallion design patterns
- Data modeling for analytical, operational, and ML workloads
- Metadata management, lineage, observability, and cost optimization
- MLOps, feature stores, model versioning, and deployment strategies
- AI system design fundamentals including LLM integration patterns and vector-based retrieval
- Deep experience designing and operating cloud-native data and AI platforms on AWS, Azure, or GCP
- Experience working across multi-cloud environments
- Strong understanding of networking, storage, identity, GPU workloads, and security boundaries in cloud data and AI systems
- Collaboration, prioritization, and RAID ownership across multiple engagements
- Comfortable operating in ambiguity and creating clarity for teams
- Ability to influence senior stakeholders as a trusted outsider
- Strong facilitation, alignment, and decision-making capability
- Operates as a high-performing remote leader ensuring work is visible, transparent, and uplifting to peers
- Delivery-first and outcome-oriented (get shit done mentality)
- Creative and open to new approaches, including emergent AI technologies
- Comfortable working in ambiguity and creating clarity
- Influential presence: able to shape direction across client and internal environments
- Curious, adaptable, emotionally aware, and committed to delivery excellence
Non-Negotiable Technical Skills
- 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 must demonstrate proficiency in all of the following:
(Candidates missing these will not be considered further.)
Top Skills
What We Do
RAVL was founded by four partners who believe that the key to building exceptional technology that stands the test of time is to build, train and mentor excellent Technologists for Financial Services clients. At RAVL, we are disrupting the Technology Services industry by building better technology and technologists. We specialize in full-stack architecture and development, core systems modernization, Microservices and APIs, DevSecOps & Developer experience, Cloud Platform & tooling, and Site Reliability Engineering. Build. Better.
