Senior Data Engineer

Role: Senior Data Engineer (Contract) • We're looking for a senior data engineer to embed with our team and contribute across the full data stack. The work spans pipeline development, dimensional modeling, semantic layer development, and AI-assisted engineering workflows within a DTC ecommerce environment. Core requirements: • Python-based data pipeline development — building and maintaining extraction and transformation pipelines using frameworks like dlt (data load tool) for REST API connectors and custom ingestion logic. • Strong dimensional modeling skills rooted in Kimball methodology — designing and building fact/dimension tables, handling slowly changing dimensions, and structuring marts for analytics consumption. • Semantic layer development — experience defining metrics, dimensions, and entities in dbt's semantic layer (MetricFlow) or similar frameworks. Ability to translate business logic into governed, reusable metric definitions that serve as the single source of truth for downstream analytics and BI tools. • Advanced SQL for both transformation logic and ad-hoc analysis within a cloud data warehouse environment. Platform experience (required): • Snowflake as the primary warehouse, Apache Airflow (MWAA) for orchestration, dbt Cloud for transformation and testing, Fivetran for managed connector ingestion, and dlt for custom Python-based connectors. • Domain experience (required): • Supply chain and logistics — experience modeling 3PL data including inventory, fulfillment, and warehouse operations. Understanding of how to integrate third-party logistics provider feeds into a centralized warehouse. • Marketing and ecommerce — experience with Shopify data (orders, subscriptions, products, discount structures), RudderStack or similar CDPs for event collection and routing, and web/product event data (clickstream, conversion funnels, attribution). Comfortable working with high-volume event streams and the nuances of marketing data (UTM parsing, campaign hierarchy, multi-touch attribution models). • Preferred experience: • Familiarity with AI-augmented engineering workflows — using LLMs for code generation, semantic layer development, or automated documentation within data engineering contexts. Comfort working in environments where AI tooling is part of the day-to-day process, not a novelty. • What the work looks like: • Building new data pipelines end-to-end (source → staging → marts), contributing to a dbt monorepo with established conventions and CI/CD, authoring and maintaining semantic layer definitions (metrics YAML, entity relationships, dimension mappings), and operating within a governed architecture where data engineering owns the core modeling layer. The person should be comfortable with code review rigor.