Data Engineer I developing and maintaining scalable data pipelines for Finning. Collaborating with senior engineers and processing diverse datasets for business impact.
About the role
- Principal Data Engineer at RAVL designing secure and scalable data and AI platforms. Leading architectural guidance and building capabilities across the organization to enhance data operations.
Responsibilities
- 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.
Requirements
- Strong Grasp of Core Data & AI Engineering Concepts
- 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
- Cloud-Native & Multi-Cloud Architecture
- 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
- Consulting Excellence
- 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
- Mindset Success Traits (Mandatory)
- 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.
Benefits
- Equal Opportunity & Accessibility
- Accommodations throughout the hiring process upon request
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