Principal Data Engineer

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?

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

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.)