Strategic Data & Analytics Engineer (Cloud Data & Agentic Infrastructure)

Shift Paradigm is an execution-led strategic consultancy that bridges enterprise strategy and technical reality through data, marketing, and technology expertise. The company is seeking a consultative Data & Analytics Engineer to design and maintain enterprise marketing data models, semantic layers, and warehouse-native infrastructure. The role also focuses on preparing data ecosystems for autonomous AI agents through cloud platform architecture, cross-system integrations, identity resolution, and data governance.

Responsibilities:

• Consultative Requirements & Architecture: Partner with key business stakeholders to uncover core business goals, mapping complex business domain needs into unified data engineering solutions, semantic layers, and robust warehouse architectures
• Agentic & AI Data Readiness: Architect forward-looking context layers and semantic models designed specifically for AI/LLM-driven engines and autonomous agents—ensuring data ecosystems (from modern cloud warehouses to activation platforms like Hightouch and Salesforce) are structured for reliable execution
• Taxonomy & Metadata Engineering: Design scalable data models (e.g. Medallion/Multi-layer architectures) to house controlled classification hierarchies, unified asset tags, and standardized business glossaries across marketing channels
• Identity & Semantic Modeling: Build and maintain deterministic and probabilistic identity resolution models and graph-like bridge tables to resolve customer identities across CRM IDs, device tokens, and digital touchpoints
• Cross-System Pipeline Integration: Lead the technical integration of complex cross-system datasets, developing high-performance ingestion and transformation pipelines to unify marketing metadata, CDP activation models, DAM content, and real-time telemetry
• Data Governance & Quality: Implement enterprise governance, business glossaries, access controls, and data cataloging (e.g., Unity Catalog, Snowflake Horizon) to enable secure self-service analytics and reliable AI execution

Requirements:

• 5+ years of hands-on experience in data engineering, analytics engineering, or data architecture building production-grade data pipelines and semantic models
• 2+ years in a client-facing, consultative, or strategic lead role—demonstrated ability to run discovery, map business goals to technical architectures, and present solutions to cross-functional stakeholders
• Willing to travel 25% a year and up to twice a quarter minimum
• Consultative Mindset: Strong ability to navigate ambiguity, translate non-technical business strategies into clear technical specifications, and articulate technical trade-offs to executives and cross-functional teams
• Strategic Vision for AI/Agents: A keen interest in the shift toward agentic AI, with an understanding of how data modeling, semantics, and context layers must adapt to support autonomous workflows across data platforms and marketing engines
• Cloud Data Platform Expertise: Hands-on experience designing production pipelines and semantic data models across modern cloud data platforms, with deep expertise in Databricks, Snowflake, or equivalent architectures
• Advanced Data Engineering Skills: Proficiency in SQL, Python/PySpark, data pipeline orchestration, and modern data stack tooling (e.g., dbt)
• Domain & Integration Knowledge: Experience with marketing technology (MarTech), Customer Data Platforms (CDPs), reverse ETL tools (e.g., Hightouch, Census), and enterprise platforms (e.g., Salesforce Marketing Cloud, CRMs)
• Data Modeling Fundamentals: Proven track record in multi-dimensional schema design, identity resolution modeling, graph/hierarchical structures, and API payload parsing (nested JSON/REST)
• Forward-Thinking AI Perspective: Active interest and practical awareness of agentic AI concepts (e.g., tool use, LLM context retrieval, autonomous agent workflows) and how data modeling must evolve to serve them
• Remote & Autonomous: Ability to manage complex deliverables independently in a distributed, fast-paced environment
• Candidates must have current US or Canada work authorization
• No relocation assistance can be offered at this time
• Relevant Certifications (Preferred): Professional Data Engineer / Analytics Engineer certifications in Databricks or Snowflake
• Databricks: Certified Data Engineer Professional or Certified Analytics Engineer
• Snowflake: SnowPro Core or SnowPro Advanced Architect
• dbt: dbt Cloud Developer Certification