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

The Role  We are seeking a forward-thinking, consultative Data & Analytics Engineer to design, build, and maintain enterprise marketing data models, ontologies, and semantic context layers. In this role, you will act as a strategic bridge between business leaders and technical execution—partnering directly with clients to understand their strategic goals and translating those business requirements into scalable, warehouse-native data infrastructure. Beyond core data engineering, you will spearhead how our clients' data ecosystems adapt to an evolving agentic future. You will architect the semantic models and data integration patterns required to power autonomous AI agents, both native to modern cloud data platforms (such as Databricks and Snowflake) and across connected MarTech platforms (such as Salesforce, Hightouch). What You'll Accomplish  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. What You Bring Experience & Expertise 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…