Google BigQuery Data Lake Architect Consultant

• Initial duration: 3 to 6 months, with strong possibility of extension.
• US-based candidates only.
• Must be able to work in the Pacific Time Zone.
• Consulting role requiring hands-on architecture and implementation leadership.

The consultant should be comfortable integrating data from a heterogeneous retail technology environment, including platforms such as:

• Shopify Plus
• Manhattan POS
• Aptos Merchandising and Allocation
• Aptos WMS
• Aptos CRM
• Aptos Sales Audit
• NewStore OMS
• NetSuite Financials
• Listrak
• ProShip

• Google Cloud Platform, * Design the overall Google Cloud data lake / lakehouse architecture, with BigQuery as the core enterprise analytical platform.

• Define ingestion patterns for batch, near-real-time, streaming, API, file-based, and database-source integrations.

• Establish architectural patterns using BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer / Airflow,

Datastream, Cloud Run / Cloud Functions, dbt and/or Dataform, and Dataplex.

• Develop a strategy for ingesting data from SaaS and enterprise applications into Google Cloud.

• Establish raw, standardized, curated, and consumption data layers.

• Design enterprise data models supporting analytics, reporting, AI/ML, and GenAI use cases.

• Define BigQuery standards for datasets, tables, partitioning, clustering, retention, and performance.

• Define scalable patterns for historical data, incremental processing, CDC, and slowly changing dimensions.

• Establish master and reference data standards.

• Define data-quality frameworks, reconciliation controls, observability, lineage, and monitoring.

• Establish security architecture including IAM, service accounts, row-level security, column-level security, policy tags,

encryption, PII protection, and environment separation.

• Establish BigQuery cost-management and optimization practices.

• Develop standards for CI/CD, infrastructure as code, testing, deployment, and environment management.

• Work across BI, data engineering, application, infrastructure, security, and business teams.

• Provide technical leadership and mentoring to internal engineering resources and implementation partners.

• Develop a phased migration and implementation roadmap., * Work your way - Enjoy the freedom to work from anywhere, with flexible hours that match your natural rhythm.
• Work with global clients - Collaborate directly with international teams to create real impact.
• Great people, no micromanagement - Join a supportive, results-focused team where you’re trusted to do your best work.

This flexibility allows developers…

• A better work-life balance
• Increased productivity
• The ability to work any time around the clock
• Reduction in commute time
• Design your ideal daily schedule.
• Build a career, not just a job.
• Work smarter, not longer.
• More time with family and friends

• 10+ years of enterprise data architecture, data engineering, or data-platform experience.

• 5+ years of significant Google Cloud Platform experience.

• Deep hands-on experience with BigQuery in production environments.

• Proven experience architecting a cloud enterprise data lake, lakehouse, or modern data warehouse.

• Strong knowledge of BigQuery architecture and optimization, SQL, data modeling, ELT/ETL, pipelines, APIs, CDC, streaming, data quality, metadata, lineage, and security.

• Experience designing platforms that process large transaction volumes.

• Experience with dbt and/or Dataform.

• Experience with Airflow / Cloud Composer or similar orchestration tooling.

• Experience with Git-based development and CI/CD.

• Experience implementing data governance within GCP.

• Ability to develop architecture while remaining hands-on with engineering teams.

• Strong communication skills with technical and business stakeholders.

GOOD TO HAVE:

• Retail industry experience, especially fashion, specialty, or omnichannel retail.

• Experience implementing enterprise retail data models.

• Experience migrating from legacy merchandising / ERP platforms.

• Experience building data foundations for AI, machine learning, and GenAI.

• Snowflake experience and ability to compare Snowflake and BigQuery architectural patterns.

• Experience defining semantic layers and supporting BI platforms.

• Experience managing offshore or systems-integrator development teams.