Customer Data & Lakehouse Architect

Lead current-state discovery and define target-state customer data architecture for enterprise warehouse and lakehouse environments, with deep work in Snowflake and/or Databricks.

Design scalable ingestion and ELT/ETL patterns across operational systems, CRM, transactional sources, digital events and approved third-party datasets, including incremental refresh, scheduling, lineage and data observability.

Own canonical data models for customers, accounts, households, vehicles, products, transactions, interactions and identity relationships as applicable to each client; define source precedence, golden-record logic and reliable join keys.

Apply medallion architecture where appropriate, including bronze, silver and gold layers, data quality validation, deduplication, normalization, enrichment, standardization and curated consumption datasets.

Build reusable, governed data products for customer 360, audience eligibility, suppression, measurement, attribution, experimentation, reporting and predictive features rather than one-off transformations for each campaign.

Design the integration boundary between enterprise data platforms and Salesforce Data 360, including data streams, Data Lake Objects (DLOs), Data Model Objects (DMOs), identity inputs, segmentation-ready attributes, activation feeds and zero-copy/federated access where supported and appropriate.

Define customer identity, consent, privacy, access control, data retention, permitted third-party exchanges and governance rules in partnership with client data owners, security and legal teams.

Architect campaign and customer measurement datasets, including exposure-to-outcome joins, sales matchback, attribution rules, reusable KPIs, historical continuity and approved data sharing.

Design feature engineering and recurring scoring workflows for propensity, churn, lifetime value and next-best-action use cases; prototype or implement models when expertise permits and collaborate with data scientists on evaluation, deployment and monitoring.

Translate architecture into source-to-target mappings, data contracts, transformation specifications, backlog priorities, acceptance criteria and delivery roadmaps; review implementation work by distributed data engineers.

Lead technical workshops, explain platform trade-offs and operating costs, and guide client stakeholders toward pragmatic decisions that preserve client ownership of data and analytical assets.

Partner with Salesforce, CDP, marketing automation, analytics and journey teams to ensure data products can be used reliably and compliantly in activation platforms.

8+ years of experience in data architecture, data engineering or analytics engineering, including senior ownership of production customer or enterprise data solutions.

Deep hands-on architecture and production experience in Snowflake OR Databricks, with strong knowledge of warehouse/lakehouse storage, transformation, performance, governance and cost management.

Advanced SQL and practical Python proficiency, including the ability to validate data, prototype transformations, investigate pipeline failures and review technical implementation.

Proven experience with dimensional and relational modeling, canonical customer data models, ELT/ETL, data quality, medallion/layered architectures and reusable data products.

Working knowledge of Salesforce Data 360 (formerly Data Cloud), including DLOs, DMOs, data streams, identity resolution, segmentation and downstream Salesforce Marketing Cloud activation; ability to collaborate effectively with hands-on Salesforce specialists.

Strong understanding of customer identity, profile unification, deduplication, consent, source precedence, household or account relationships and privacy-aware data sharing.

Ability to define technical architecture and lead implementation by engineers while staying hands-on when investigation, prototyping or delivery validation is required.

Experience translating business and analytical needs into data models, integration contracts, architecture decisions and clear documentation for technical and business stakeholders.

Production experience with both Snowflake and Databricks; knowledge of Spark/PySpark, Delta Lake, Apache Iceberg, Unity Catalog or similar metadata, catalog and interoperability capabilities.

Direct implementation experience or Salesforce Data Cloud Consultant certification; familiarity with zero-copy/federated patterns and consumption-aware design between cloud data platforms and Data 360.

Experience with dbt, Airflow or comparable orchestration, automated data testing, CI/CD, monitoring and data catalog or observability solutions.

Applied data science and machine learning experience, especially feature engineering, propensity modeling, churn prediction, lifetime value estimation, forecasting, experimentation or production scoring.

Experience with additional CDPs or activation platforms such as Tealium, Hightouch, Segment, Adobe Experience Platform or Adobe Real-Time CDP.

Automotive, OEM or dealer-network experience, including vehicle ownership, VIN and household relationships, registrations, dealership data, conquest suppression or sales matchback.

Experience coordinating approved third-party identity, enrichment, address hygiene, measurement or data providers.

Background in consulting or agency delivery, client-facing architecture leadership and mentoring distributed or nearshore data engineering teams.